{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Model Comparison for Cognitive Models\n",
    "\n",
    "Part 2: Hierarchical Model Comparison\n",
    "\n",
    "by Lasse Elsemüller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Generative-Model-Definition\" data-toc-modified-id=\"Generative-Model-Definition-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Generative Model Definition</a></span><ul class=\"toc-item\"><li><span><a href=\"#Hyperpriors-and-Priors\" data-toc-modified-id=\"Hyperpriors-and-Priors-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Hyperpriors and Priors</a></span></li><li><span><a href=\"#Creating-the-Simulators\" data-toc-modified-id=\"Creating-the-Simulators-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Creating the Simulators</a></span></li><li><span><a href=\"#Prior-Predictive-Checks\" data-toc-modified-id=\"Prior-Predictive-Checks-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Prior Predictive Checks</a></span></li></ul></li><li><span><a href=\"#Defining,-Training-&amp;-Validating-the-Neural-Approximator\" data-toc-modified-id=\"Defining,-Training-&amp;-Validating-the-Neural-Approximator-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Defining, Training &amp; Validating the Neural Approximator</a></span></li><li><span><a href=\"#Network-Application\" data-toc-modified-id=\"Network-Application-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Network Application</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from scipy import stats\n",
    "\n",
    "import bayesflow as bf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction \n",
    "\n",
    "This is the second part of the tutorial series covering amortized model comparison with BayesFlow! The general workflow, the scenario and the cognitive models were introduced in [Part 1](./Model_Comparison_MPT.ipynb) and are assumed to be known, so here we will focus on the new elements introduced when comparing hierarchical models.\n",
    "\n",
    "In [Part 1](./Model_Comparison_MPT.ipynb), we only conducted model comparison for a single participant at a time. Let us now consider all participants and their nested observations simultaneously in our model comparison!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generative Model Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extend our MPT models to hierarchical ones, we need to introduce a superordinate level that encodes our assumptions about the relationships between individuals. We use the most popular hierarchical MPT framework, the latent-trait approach by [Klauer (2010)](https://link.springer.com/article/10.1007/s11336-009-9141-0). Here, we replace our non-hierarchical Beta priors by a multivariate normal distribution, which allows us to model correlations between our parameters. We afterwards use the cumulative distribution function of the standard normal distribution, $\\Phi$, to transform from the real-line to probabilities. Let's write out our new model components explicitly with $m \\in M$ denoting the participants:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align}\n",
    "\\left[ \\begin{array}{l} d_m' \\\\ g_m' \\end{array} \\right] \n",
    "&\\sim \\mathcal{N} \\left( \n",
    "\\left[\\begin{array}{l} \\mu_{d} \\\\ \\mu_{g} \\end{array} \\right], \\Sigma\n",
    "\\right) \\text{ for } m=1,\\dots,M\\\\\n",
    "d_m &= \\Phi(d_m') \\text{ for } m=1,\\dots,M\\\\\n",
    "g_m &= \\Phi(g_m') \\text{ for } m=1,\\dots,M\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperpriors and Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have to define hyperpriors for the parameters of the multivariate normal prior distribution. For the covariance matrix $\\Sigma$, the latent-trait approach employs a scaled inverse Wishart distribution. The $Q$ parameter controls the correlation between our parameters $d$ and $g$, while the variances are determined jointly with the scaling parameters $\\lambda$.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\mu_{d} &\\sim \\mathcal{N}(0, 0.25) \\\\\n",
    "\\mu_{g} &\\sim \\mathcal{N}(0, 0.25) \\\\\n",
    "Q &\\sim InvWishart(\\mathbb{I}, 10)\\\\\n",
    "\\lambda_p &\\sim \\textrm{Uniform}(0, 3) \\text{ for } p= d', g'\\\\\n",
    "\\Sigma &= \\textrm{Diag}(\\lambda_p) Q \\textrm{Diag}(\\lambda_p)\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we choose our priors to reflect our belief that the hierarchical models should generate data patterns similar to their non-hierarchical counterparts. Remember that Bayesian model comparison penalizes predictive flexibility and expects you to encode your theoretical assumptions in all parts of your models. Therefore, using flat/very weakly informative priors as you may do in parameter estimation won't give you the results you are looking for here. \n",
    "\n",
    "Things can get a little confusing for these hierarchical model formulations, so let's have a look at the role of our prior choices:\n",
    "- $\\mu_d$ and $\\mu_g$: A zero-centered normal distribution on the probit scale translates to participant mean values centered around 0.5 on the probability scale.\n",
    "- $Q$: An inverse Wishart distribution with an identity scale matrix centers the expected correlations between $d$ and $g$ at 0, while the 10 degrees of freedom encode our belief that high correlations are rather unlikely (see below for a visualization).\n",
    "- $\\lambda$: We keep the values of this auxiliary scaling parameter rather low to limit the amount of variability that we introduce into our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q = stats.invwishart.rvs(df=10, scale=np.identity(2), size=5000)\n",
    "corrs = Q[:, 0, 1] / (np.sqrt(Q[:, 0, 0] * Q[:, 1, 1]))\n",
    "f, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "sns.histplot(corrs, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax)\n",
    "ax.set_title(\"Inverse Wishart Correlation Prior\")\n",
    "sns.despine(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now follow the same steps as in [Part 1](./Model_Comparison_MPT.ipynb) to translate our prior into code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAM_NAMES = [r\"$\\mu_d$\", r\"$\\mu_g$\", r\"$\\Sigma_{00}$\", r\"$\\Sigma_{01}$\", r\"$\\Sigma_{10}$\", r\"$\\Sigma_{11}$\"]\n",
    "RNG = np.random.default_rng(2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hierarchical_prior_fun(rng=None):\n",
    "    \"\"\"Samples a random parameter configuration from the hierarchical prior distribution.\"\"\"\n",
    "\n",
    "    if rng is None:\n",
    "        rng = np.random.default_rng()\n",
    "\n",
    "    mu_d = rng.normal(0, 0.25)\n",
    "    mu_g = rng.normal(0, 0.25)\n",
    "    Q = stats.invwishart.rvs(df=10, scale=np.identity(2), random_state=rng)\n",
    "    lambdas = rng.uniform(0, 3, size=2)\n",
    "    sigma = np.matmul(np.matmul(np.diag(lambdas), Q), np.diag(lambdas))\n",
    "    return np.concatenate([np.r_[mu_d, mu_g], sigma.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prior_draws': array([[-0.27184395,  0.04654362,  0.63897366,  0.08509082,  0.08509082,\n",
       "          0.29657829]]),\n",
       " 'batchable_context': None,\n",
       " 'non_batchable_context': None}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior = bf.simulation.Prior(prior_fun=hierarchical_prior_fun, param_names=PARAM_NAMES)\n",
    "prior(batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Simulators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, it is important to stress again our new definition of a data set: In [Part 1](./Model_Comparison_MPT.ipynb), we analyzed participants separately, so each data set contained a single participant. With hierarchical models, we can now take all of our experimental data simultaneously into consideration, so each data set contains several participants with nested observations each. We could also have other hierarchical models, such as students nested into classes or employees nested into organizations, so we refer to the higher order units with the general term *groups*. For this tutorial, we consider a scenario with 50 participants performing 100 trials each.\n",
    "\n",
    "We continue our known workflow by first specifying simulator functions, then creating our generative models with the ``GenerativeModel`` wrapper and finally combining them with the ``MultiGenerativeModel`` wrapper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_GROUPS = 50\n",
    "N_OBS = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def hierarchical_mpt_simulator(theta, model, num_groups, num_obs, rng=None, *args):\n",
    "    \"\"\"Simulates data from a hierarchical 1HT or 2HT MPT model, assuming equal proportions of old and new stimuli.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    theta      : np.ndarray of shape (num_parameters, )\n",
    "        Contains draws from the prior distribution for each parameter.\n",
    "    model      : str, either \"1HT\" or \"2HT\"\n",
    "        Decides the model to generate data from.\n",
    "    num_groups : int\n",
    "        The number of groups (participants).\n",
    "    num_obs    : int\n",
    "        The number of observations (trials) per group.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    X     : np.ndarray of shape (num_groups, num_obs, 2)\n",
    "        The generated data set. Contains two columns:\n",
    "            1. Stimulus type (0=\"new\", 1=\"old\")\n",
    "            2. Response (0=\"new\", 1=\"old\")\n",
    "    \"\"\"\n",
    "\n",
    "    if rng is None:\n",
    "        rng = np.random.default_rng()\n",
    "\n",
    "    obs_per_condition = int(np.ceil(num_obs / 2))\n",
    "\n",
    "    mu_d, mu_g = theta[:2]\n",
    "    sigma = np.reshape(theta[2:], (2, 2))\n",
    "\n",
    "    # Draw vectors containing individual parameters and transform to probabilities\n",
    "    params = rng.multivariate_normal([mu_d, mu_g], sigma, size=num_groups)\n",
    "    d = stats.norm.cdf(params[:, 0])\n",
    "    g = stats.norm.cdf(params[:, 1])\n",
    "\n",
    "    # Compute category probabilities per model\n",
    "    if model == \"1HT\":\n",
    "        p_11 = d + (1 - d) * g\n",
    "        p_10 = (1 - d) * (1 - g)\n",
    "        p_01 = g\n",
    "        p_00 = 1 - g\n",
    "\n",
    "    if model == \"2HT\":\n",
    "        p_11 = d + (1 - d) * g\n",
    "        p_10 = (1 - d) * (1 - g)\n",
    "        p_01 = (1 - d) * g\n",
    "        p_00 = d + (1 - d) * (1 - g)\n",
    "\n",
    "    # Assert that category probabilities sum to 1\n",
    "    assert np.all(np.isclose((p_11 + p_10, p_01 + p_00), 1)), \"Category probabilities do not sum to 1!\"\n",
    "\n",
    "    # Create vectors of stimulus types\n",
    "    stims_single = np.repeat([[1, 0]], repeats=obs_per_condition, axis=1)  # For 1 participant\n",
    "    stims_data_set = np.repeat(stims_single, repeats=num_groups, axis=0)  # For all participants\n",
    "\n",
    "    # Simulate responses\n",
    "    resp_1 = rng.binomial(n=1, p=p_11, size=(obs_per_condition, num_groups)).T\n",
    "    resp_0 = rng.binomial(n=1, p=p_01, size=(obs_per_condition, num_groups)).T\n",
    "    resp = np.concatenate((resp_1, resp_0), axis=1)\n",
    "\n",
    "    # Create final data set\n",
    "    data = np.stack((stims_data_set, resp), axis=2)\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing 2 pilot runs with the 1HT model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 6)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 50, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n",
      "INFO:root:Performing 2 pilot runs with the 2HT model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 6)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 50, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n"
     ]
    }
   ],
   "source": [
    "model_1ht = bf.simulation.GenerativeModel(\n",
    "    prior=prior,\n",
    "    simulator=partial(hierarchical_mpt_simulator, model=\"1HT\", num_groups=N_GROUPS, num_obs=N_OBS),\n",
    "    name=\"1HT\",\n",
    "    simulator_is_batched=False,\n",
    ")\n",
    "\n",
    "model_2ht = bf.simulation.GenerativeModel(\n",
    "    prior=prior,\n",
    "    simulator=partial(hierarchical_mpt_simulator, model=\"2HT\", num_groups=N_GROUPS, num_obs=N_OBS),\n",
    "    name=\"2HT\",\n",
    "    simulator_is_batched=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We added the *group* dimension to our data sets, so we now have our data in a 4-dimensional format with the shape (number of data sets, number of groups/participants, number of observations, number of variables):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data batch: (5, 50, 100, 2)\n",
      "First 3 rows of first 2 participants in first data set:\n",
      "[[[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]\n",
      "\n",
      " [[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]]\n"
     ]
    }
   ],
   "source": [
    "model_output = model_1ht(batch_size=5)\n",
    "print(\"Shape of data batch:\", model_output[\"sim_data\"].shape)\n",
    "print(\"First 3 rows of first 2 participants in first data set:\")\n",
    "print(model_output[\"sim_data\"][0, :2, :3, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_model = bf.simulation.MultiGenerativeModel([model_1ht, model_2ht])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior Predictive Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interplay between parameters on several levels adds more complexity to the behavior of hierarchical models. Therefore, prior predictive or pushfoward checks become even more crucial for inspecting whether the chosen parametrization matches one's expectations. Our simulated data sets now contain ``NUM_GROUPS`` participants and ``NUM_OBS`` observations each. If we want to simulate 1000 participants as in [Part 1](./Model_Comparison_MPT.ipynb), we now need only 20 simulations from a model, as each data set contains 50 participants. Afterwards, we calculate the hit rates and false alarm rates for each simulated participant as before and plot them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Data simulation from each model\n",
    "sim_pfcheck_1ht = model_1ht(batch_size=20)\n",
    "sim_pfcheck_2ht = model_2ht(batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Summary statistics\n",
    "def get_rates(sim_data):\n",
    "    \"\"\"Get the hit rate and false alarm rate per participant for each data set in a batch\n",
    "    of hierarchical data sets simulating binary decision (recognition) tasks.\n",
    "    Assumes first half of data to cover old items and second half to cover new items.\"\"\"\n",
    "\n",
    "    obs_per_condition = int(np.ceil(sim_data.shape[-2] / 2))\n",
    "    hit_rates = np.mean(sim_data[..., :obs_per_condition, 1], axis=2)\n",
    "    fa_rates = np.mean(sim_data[..., obs_per_condition:, 1], axis=2)\n",
    "\n",
    "    return hit_rates, fa_rates\n",
    "\n",
    "\n",
    "rates_1htm = get_rates(sim_pfcheck_1ht[\"sim_data\"])\n",
    "rates_2htm = get_rates(sim_pfcheck_2ht[\"sim_data\"])\n",
    "rates = [rates_1htm, rates_2htm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3a. Plot rates across all data sets\n",
    "fig = plt.figure(constrained_layout=True, figsize=(8, 6))\n",
    "subfigs = fig.subfigures(nrows=2, ncols=1)\n",
    "model_names = [\"Hierarchical 1HT MPT Model\", \"Hierarchical 2HT MPT Model\"]\n",
    "num_bins = 20\n",
    "bins = np.linspace(0.0, 1.0, num_bins + 1)\n",
    "\n",
    "for row, subfig in enumerate(subfigs):\n",
    "    subfig.suptitle(model_names[row], fontsize=18)\n",
    "    axs = subfig.subplots(nrows=1, ncols=2)\n",
    "    sns.histplot(rates[row][0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[0]).set(\n",
    "        title=\"Hit Rates\"\n",
    "    )\n",
    "    sns.histplot(rates[row][1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[1]).set(\n",
    "        title=\"False Alarm Rates\"\n",
    "    )\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot our 1000 participants over all data sets, we see simular patterns as in [Part 1](./Model_Comparison_MPT.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining, Training & Validating the Neural Approximator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We adapt the neural network architecture to this new symmetry by changing only a single part, the summary network. We now use a ``HierarchicalNetwork`` which we pass one summary network for each level. The majority of hierarchical models in cognitive modeling assume IID data on all levels, so we simply use one ``DeepSet`` network for each level. If we would have, for instance, temporal dependencies within each participant, we would exchange the first network to one that is specialized for processing time series data, such as a ``TimeSeriesTransformer``.\n",
    "\n",
    "The first summary network summarizes the information contained within the participants separately and passes the resulting embeddings to the second summary network. The second network then further compresses all participant embeddings to a single vector of learned summary statistics, so we equip it with a sufficient number of summary dimensions to avoid a bottleneck.\n",
    "\n",
    "After these adjustments, all subsequent elements of the training and validation process stay the same as in [Part 1](./Model_Comparison_MPT.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_net = bf.summary_networks.HierarchicalNetwork([\n",
    "    bf.networks.DeepSet(), \n",
    "    bf.networks.DeepSet(summary_dim=64)\n",
    "])\n",
    "inference_net = bf.inference_networks.PMPNetwork(num_models=2)\n",
    "amortizer = bf.amortizers.AmortizedModelComparison(inference_net, summary_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing a consistency check with provided components...\n",
      "INFO:root:Done.\n"
     ]
    }
   ],
   "source": [
    "trainer = bf.trainers.Trainer(amortizer=amortizer, generative_model=meta_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Online learning will be a bit slower than what we observed in [Part 1](./Model_Comparison_MPT.ipynb) due to the slower data generation, but still worth it due to amortization. :-) In practice, you would train longer than just $5$ epochs and may want to try offline learning as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = trainer.train_online(epochs=5, iterations_per_epoch=100, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diag_plot = bf.diagnostics.plot_losses(train_losses=losses, moving_average=True, ma_window_fraction=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some validation data in a list to avoid memory troubles during evaluation\n",
    "sim_data = [trainer.configurator(meta_model(50)) for _ in range(20)]\n",
    "\n",
    "# Get true indices and predicted PMPs from the trained network\n",
    "sim_indices = np.concatenate([s[\"model_indices\"] for s in sim_data])\n",
    "\n",
    "# Estimate model probs in a loop\n",
    "model_probs = np.concatenate([amortizer.posterior_probs(s) for s in sim_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FRVWuiIiIvRgWCzu+/JLlvXrdqoMrVeL5BQv4ZOVKFi5ciIuLCwEBAc4OU13KRUREbGEYBru/+YY/Jk0CoMyjj9Li449xz5OHjz76iClTpgDw0ksv8e677yrhFhERsZOYq1f5dfhwzvzxBwDV27Wj7oAB9O3Xj61bt+Lu7s5XX31FixYtnBypEm4REZF0Mycmsum99zj4/fcA1H7xRR4dMQLDZOKtt95i3rx5ALz55psMGDDAOmmaiIiIZMyFPXv46Y03uHHpEm5eXjw+ZgxFH3mEzl26sH//fvLkycOcOXNo3Lixs0MFlHCLiIikS1x0ND8NGsTZbdvAZOLRESOo060bAG+//Tbz5s3DZDLxwQcf0LVrVydHKyIikjMYhsGeOXP44+OPMcxmCpYtyzOffUb+smV54oknOH78OIUKFWLBggXUqlXL2eFaqX+biIhIGkWdP8+Szp05u20b7j4+tJo2zZpsA3Tt2hU/Pz+mT5+uZFtERMRO4qKi+OH11/n9o48wzGYqP/MMnZYupUilSri7u/PKK69QokQJVq1alaWSbVALt4iISJpc/PtvfnjtNWIiIshTtCitp0+naLVqWCwW6/jsKlWq8Oeff+Lj4+PkaEVERHKGSwcO8OOgQUSHhuLq7s6jb79NzQ4dMAzDekynTp1o3bp1lqx/1cItIiJyH8d/+eXWTOQREfhVrUqn77+naLVqXLhwgaeffprt27dbj82Klb2IiEh2YxgGfy9YwJLOnYkODSV/yZJ0WLSIWh078ttvv/G///2PiIgI6/FZtf5Vwi1pkpiY6OwQREQynWEY/DVjBj8OGoQ5Pp6yTZvSfv58fP39CQkJoVWrVhw8eJARI0ZgNpudHa7kQKp/RSQ3Srhxg5+HDGHTe+9hTkyk/JNP0mnZMopWr87q1avp1q0bBw4c4PPPP3d2qPelLuVyh4SEBA4dOsT+/fvZv38/Bw4c4MCBA0RGRhIYGEitWrXu+Clfvrxm3xWRHMmckMDG997jn6VLAQjq2pUmb72Fi6sr+/bt48UXXyQyMpLy5cszf/58XF1dnRyxZGeqf0VEbrly9Cg/DRxI5OnTuLi50XjYMOp064bJZOLbb79l5MiRGIZBq1atGDVqlLPDvS8l3LnY+fPnrRX7/v37OXr0KGazmXLlylG5cmVq1KhBu3btqFy5MgUKFOD8+fMcOXKEo0ePsmnTJqZPn05oaCj58+enRo0ad3wRKFCggLPfnoiIzeKiovhx4EDObd+OycWFR0eOJKhLFwB+//13evXqxc2bN6lduzbz58+ncOHCTo5YshPVvyIiKftn2TI2vvce5vh4fAMCaPHppxSvUwfDMPjkk0+YPHkyAN27d2f8+PHZ4mG3Eu5cICYmhoMHD1qfmF+6dIn4+Hj8/PyoUqUKNWvWpF27dpQtW/aeH9qSJUtSsmRJnnzyyTu2x8bGcvToUY4ePcrmzZuZMWMGMTExeHl5UaZMGeuXgMqVK2eLXwoRyd2izp1jZd++RJ48ibuPDy0+/ZSyjz4KwJo1a+jXrx+JiYk0btyYb775Bl9fXydHLFmV6l8RkbRJjI1l03vvcWjFCgDKNGnCUx9+iHfBglgsFt555x2++eYbAAYPHsyQIUOyTS8fJdw52NKlS9m9ezdubm5UrFiRmjVr0r59e/Lnz2/X+3h7exMUFERQUNBd+0JDQzl69Chbtmxh7ty5uLq60q5dO+rUqWPXGERE7OHC3r380K8fsVev4hsQQOvp0/GrUsW6/8cffyQxMZFnnnmGadOm4eHh4cRoJatS/SsiknZXT57kx4EDiTh+HJOLCw8NHEj9l1/GdHsFkOjoaDZu3AjA+PHj6dWrlzPDTTcl3DlYu3btaNeunVNjCAwMJDAwkMcff9ypcYiI3M/Rn35i7VtvYU5IoGi1arT66it8/f3vOOazzz6jXr169OjRQy2GkirVvyIiaXNkzRo2vPMOiTEx+Pj58b/JkynZsOEdxxQoUIBFixaxb98+WrVq5aRIbadZykVEJFczDIOd06fz8+DBmBMSKPf449aZyC0WCytWrMBisQDg4eFB7969lWyLiIhkQFJ8PBvHjuWXoUNJjImhRMOGvLh8uTXZjoqKYv369dbjS5UqlS2TbVDCneOcPn0ak8l0z58yZcqkeO7u3bvp3bs3FStWJE+ePHh7e1O+fHm6du3KunXr7HKPzPbXX3/RokULChQoQJ48eXjwwQdZsmRJplxr/vz59O3bl3r16uHp6YnJZCI4ODjFY4ODg+9bps2aNbMpbhFJnTkhgXUjR7JtyhQA6nTvzrNffIG7jw+JiYkMGjSIfv36MXbsWOcGKlme6t87qf4VkdREnTvHkk6d2L9wIZhMNHztNdp+8w15/PwAuHTpEs899xw9e/a8429gdqUu5TlU+fLl6XJ7Rt3/+u8MphaLhaFDh/Lpp5/i5ubG448/TqtWrXB3d+fkyZP8+OOPzJ8/n/fee4/Ro0fbdA9n2LRpE0899RReXl507NiRvHnzsmzZMjp06MC5c+cYMmSIQ681atQozpw5Q5EiRShWrBhnzpxJ9fpBQUGMGTMmxX1Lly7ln3/+4amnnkpzvCJyf3HXrrFmwADO79yJydWVpqNGUbtTp1v74uLo27cv69atw9XVlZo1azo5WskuVP+q/hWR1IWsW8fakSNJuH4d74IFeeqjjyjzyCPW/adPn7b+fvv7+1OyZEknRmsnhqQqKirKAIyoqKgU9yclJRmHDx82kpKSMjmy1J06dcoAjKeeeirN54wYMcIAjKCgICMkJOSu/TExMcZHH31kvPnmmzbfIzWOKsPExESjfPnyhqenp7F3717r9mvXrhmVKlUyPDw8jNOnTzv0WuvWrbNuf//99w3AmDNnTrreR3x8vFG4cGHDzc3NCAsLS/GYrPg5zG5UhvaRncox8swZI/ipp4xPK1c2pj3wgHHqt9+s+6Kioow2bdoYxYoVM8qUKWOsXbs20+LKTmXoKNmx7jUM1b/JVP9KeqgMMy67lGFSQoKx5f33jU8rVzY+rVzZWNSxoxF98eIdxxw4cMCoWbOmUaxYMeOhhx4yzpw5kzmxObgM1aU8lwsJCeGjjz6icOHC/PLLL5QvX/6uY7y9vRk2bFi26lK5ceNGTpw4QefOne+YvTV//vyMHDmShIQEvv32W4de64knnqB06dIZeh8rV64kIiKCZ599Fv//TN4kIra5sGcPi154gcjTp8lbrBgvLFhgfbp++fJl2rZty44dO8iXLx+LFy++aykmEXtQ/eu4a6n+Fclarl+8yNKuXdlze2jHAz170m7uXPIGBFiP+fPPP3n++ecJDw+nRo0arFq1ilKlSjkpYvtSl/JcLjg4GLPZTN++fe9boXh6emZSVBm3efNmAJo3b37XvuSuYVu2bMn0a6XXrFmzAHjppZcccn2R3ObImjWsGzECc2Ii/jVq0OrLL8lTtCgASUlJvPDCCxw7doyiRYuycOFCqlat6uSIJadS/Zu510ov1b8i9nHqt9/4dfhw4q5dwzNfPpq//z7l/zMvwvHjx+nUqRMJCQk89NBDzJkzh3z58jkpYvtTwp1DhYSE8O6776a478EHH+Tpp58GYOvWrQA2LRuS1nvcz7fffou7uzsuLmnrcNGmTZsU1xz9t+PHjwNQsWLFu/YFBATg6+trPeZ+7Hmt9Dhz5gwbNmygRIkSaS5LEUmZYRjs/Oor/vz8cwDKP/kkT3/4Ie4+PtZj3NzcGDZsGBMnTuS7777LMpNPSfai+lf1r4iAJSmJ7VOnsnP6dACKVq/OM599Rv4SJe46tkKFCnTs2JFLly4xffr0bPWQMS2UcOdQJ06cSLUL2sCBA60VSFhYGAAlUvjw2+se9zN37lwuXLiQ5vuWKVPmvhV+VFQUcKvbWUry5ctnPeZ+7Hmt9JgzZw4Wi0Xr/YpkUFJCAhtGj+bwqlUA1O3Vi8ZDh2K6nWQkJSXh5narOnzmmWdo3rw57u7uTotXsjfVv6p/RXK7m5cv8/PQoZzfuROA2p0788hbb+Hm4XHHccn1r8lkYsKECRiGYa2Pc5Kc944EuNXV6pdffskW99iwYQMVK1ZUpfYvFouFOXPmYDKZ6NWrl7PDEcm2YiMjWdO/P6G7dmFydeXxd96hZocO1v3r169n7NixLFq0iMDAQAAl25Ihqn+zN9W/Ihlzbvt2fh46lJjwcNx9fHhi/Hgqt2hxxzGGYfD+++9z5MgRZs+ejbu7e47+O6RJ03K5gNuTFYSGhjo5EvtKfhqe2pPv6OjoVJ+YO/JaabV+/XrOnj3L448/TtmyZe16bZHcIvL0aRZ37Ejorl14+PrS5uuv70i2ly1bRs+ePTlx4gRfffWVEyOV3Ej1b+ZeK61U/4rYxrBY2Dl9Ost79SImPJwilSrRaenSu5LtpKQkhg0bxtSpU1m/fr11roacTC3cudzDDz/M5s2b2bBhg03jyOzBEWPIksd7HT9+nLp1696xLywsjBs3btCgQYM03c+e10orTdYikjGhu3bxQ79+xEVFkbd4cVpPn06RSpWs+2fNmsU777wDQLt27VJdh1fEUVT/3p/qX5HsITYykl+GDePMH38AUP3552k6ahTu3t53HBcfH89rr73Gzz//jIuLC5MmTcoVK4Eo4c7levTowQcffMCMGTMYNGgQfn5+qR4bHx/vkEkMHDGG7NFHH+X9999n7dq1dOzY8Y59v/76q/WYtLDntdIiIiKCVatWUahQIZ577jm7XVcktzi8ejXr33771kzkNWvemon89t82wzCYNGkSU6ZMAeDll19mzJgxaU44ROxF9e/9qf4Vyfou7NnDT4MHcyMsDDcvLx575x2qt21713HXr1+nV69ebN26FXd3d6ZPn87//vc/J0Sc+fQNI5erUKECw4cPJzw8nP/973+cOnXqrmPi4uL45JNPUp0RNaM2bNhAUlIShmGk6adHjx73vWazZs0oV64cCxYsYN++fdbtUVFRTJw4EQ8PD7p163bXeSdOnODIkSMkJiZm+Fq2mjdvHgkJCXTp0iXHzdIo4kiGYfDnF1/w6/DhmBMTqdC8Oe3mzrUm22azmbfeesuabL/11lu8++67SrbFKVT/3kn1r0j2YhgGe+bMYWm3btwIC6Ng2bJ0XLw4xWQ7IiKC9u3bs3XrVnx9fVmwYEGuSbZBLdw51r2WDIFbXzS9vLwAGD9+PHFxcXz66adUrlyZxx9/nBo1auDu7s6pU6dYv349ERERjB8/3uZ7ZDY3NzdmzZrFU089RZMmTejYsSN58+Zl2bJlnDlzhsmTJ6e45E+zZs04c+YMp06dsu639VqzZs3ij9tdaw4cOGDdljxWpXHjxil2WZs9ezag7mwi6ZGUkMD6t9/myA8/AFDv5Zd5+I03rDORA8TExLBr1y5MJhMffPABXbt2dVa4koOp/lX9K5KTWMxmQnft4uaVK+Tx8yOwXj0Sbt5k7YgRnNywAYBKLVrwxHvv4eHrm+I1Lly4QEhICIULF+a7776jVq1amfkWnM+QVEVFRRmAERUVleL+pKQk4/Dhw0ZSUlImR5a6U6dOGcB9fyIjI+8696+//jJ69eplVKhQwfD29jY8PT2NMmXKGJ07dzbWrVtnl3v8l6PLcMeOHcbTTz9t5MuXz/D29jYaNGhgLFq0KNXjS5cubQDGqVOnMnyt7t2737N8unfvnuI9AKNBgwZpfo9Z8XOY3agM7SMzytGclGSc3b7dOPzDD8bZ7dsNc1KSEXP1qrG4c2fj08qVjc+qVzcOLFmS6vlhYWHGr7/+6rD4MkqfxexZ9xqG6t//Uv0raaEyzDhHluHxX381Zj76qPFp5crWn+mNGhnTGzUyPq1c2fi8Rg3j74ULDYvFct9rbd261Thx4oTdY7QHR38OTYZhGPZL33OW5Nkvo6KiyJcv3137zWYzx48f15IaGaAyzDiVYcapDO3D0eUYsnYtmydO5Mbt9YsBfIoUASAmPByPvHl59rPPKNWokXX/1atX+e2332jTpo3d43EEfRZV92YWlWPGqQwzTmWYcY4qw5C1a1kzcCCkkip6Fy7MczNmULR69RT37969GxcXF+rUqWO3mBzF0Z9DdSkXEZEsL7WKPyY8HADvQoVoN3cuhStUsO67cOECHTt2JCQkBMMwNAmSiIhIGljMZjZPnJhqsg3g4uZGkSpVUty3efNmevfujZeXF2vWrMn1S+xpphgREcnS0lrxF/xXhR4SEkKrVq0ICQmhWLFi1KhRIzNCFRERyfZCd+26ozdZSm5eukTorl13bV+9ejXdu3cnNjaWWrVqUbRoUUeFmW0o4RYRkSwtTRX/5cvWin/fvn20bt2aCxcuUL58eVavXm1dz1dERETu7eaVKzYdFxwczKuvvkpiYiKtWrVi7ty55MmTxxEhZitKuEVEJEtLT8X/+++/065dOyIjIwkKCmLVqlUEBgY6OEIREZGcI3k5zbQeZxgGH3/8MSNHjrQuITht2jTc3d0dGWa2oYRbRESyNJ/ChdN0XERiIl26dCEmJobGjRuzZMkSChUq5ODoREREcpbAevXunXSbTPgGBBBYrx4ACxYs4OOPPwZgyJAhTJgwQZPg/YsmTRMRkSwr8tQptk2Zcu+DTCZ8/f15qFUrXjlyhFOnTjF16lQ8PDwyJUYREZGcxMXVlXwlSqTcw8xkAqDpyJG43E6q27Zty7Jly2jZsiU9e/bMzFCzBSXcIiKS5RgWC/vmz2frJ5+QFBeHm6cnSfHxtyr6f02eZgBmi8Va8b/11ltYLBY9WRcREbHRiQ0buLh3L7i44FOwIDEREdZ9vv7+NB05kpKPPophGJhMJry9vfn+++9V96ZCCbeIiGQpUefOsXbECOskaKUaNeKJ8eO5fPDgHetwWwyDTfHxxAcG8nqTJgCYTCZV+CIiIjaKv36djWPHAlCvd28aDRpE6K5d3LxyhTx+fgTWq0f09eu0a9eORx55hOHDhwOo7r0HJdwiIpIlGBYL+xct4o/Jk0mMicHdx4dHhg+nZocOmEwm8hUvTrlmzQjdtYtrFy/yyXffsXvrVjh1it9++43mzZs7+y2IiIhka79PmsTNy5cpULo0D/brh4urKyUbNrTuDwsLo1OnThw9epSQkBB69Oihpb/uQwm3iIg4XXRoKOtGjeLcn38CEFi/Ps0nTiR/yZJ3HOfi6kqRWrV4e9o01m/diqurK1OmTFGyLSIikkHntm/n4JIlADwxfjxuXl537D916hQdO3bk3Llz+Pv7s2jRIiXbaaCEW0REnMYwDP5ZupTfPviAhJs3cfPy4uHBgwnq0gWTy90LaURHR9OtWzd27tyJp6cnM2fO5IknnnBC5CIiIjlHYmws6995B4CaHTtSon79O/YfPHiQzp07Ex4eTpkyZVi8eDEl//NQXFKmhFtERJzielgY60eN4swffwBQrE4dmr//PgXLlEnx+MuXL9OpUycOHz5Mvnz5mDt3Lg0aNMjEiEVERHKm7VOnEnX2LL4BATQeOvSOfX/++Sc9evTg+vXr1KhRg++++w6/NK7VLUq4RUQkkxmGweGVK9k8cSIJ16/j6uFBo0GDqNO9u3WJkZRERUVx8eJFihYtysKFC6latWomRi0iIpIzXTpwgD1z5gDw+JgxePr63rE/NDSU69ev89BDDzFnzhzy5cvnjDCzLSXcIiKSaW5evsyGMWM4uWkTAP61avHUBx9QqFy5+55bsWJFFi5cSIECBShdurSjQxUREcnxzImJrBs1CsNiofIzz1DuscfuOqZdu3bky5ePRx99FE9PTydEmb0p4RYREYczDIOja9awafx44qOicHV358H+/anbqxcubqlXRTt37iQxMZGHH34YgNq1a2dWyCIiIjne7tmzCT96FK8CBXj07bet2+fPn0/z5s2tk6JpclLbKeEWERGHiomIYOO77xKybh0ARatVo/kHH1CkUqV7nrd+/Xpefvll3NzcWL16tbqQi4iI2NHVkyfZMW0aAE3ffhufQoUwDIOJEycybdo0goODWbNmDV7/ma1c0kcJt4iIOMzxX35h49ixxEZG4uLmRoNXX6V+nz64urvf87ylS5fyxhtvYDabadKkCWVSmUhNRERE0s+wWFg/ahTmxETKPPoolZ99lqSkJN58800WLlwIQNu2bZVs24ESbhERsbvYyEg2jRvHsZ9+AqBI5co0/+ADiqahlXrmzJmMGTMGgPbt2zN58mTc75Ogi4iISNrtX7iQC3v24O7jw+NjxpCQkMBrr73Gzz//jIuLC5MnT6Zjx47ODjNHUMItIiJ2dXLjRjaNHUtMeDgmV1fq9+lDw1dfxdXD457nGYbBRx99xGeffQZAnz59eOedd3BJYT1uERERsU10aCh/fPwxAI2HDsWUNy9dunRh69ateHh4MH36dJ5++mknR5lzKOEWERG7iIuKYv+UKVzYvBmAQhUq0Pz99wmoWTNN5y9ZssSabI8YMYLXX38dk8nkqHBFRERyHcMw2PDuuyTGxFC8bl1qdezIa/36sXXrVnx9fZkzZ451olKxDyXcIiKSYae2bGH96NHcvHwZk4sLdXv35sHXX8ctHcuHtG3blp9++oknn3ySLl26ODBaERGR3OnIDz9w5vffcfXw4Ilx4zC5uDBq1ChOnjzJ5MmTqZnGh+SSdkq4RUTEZvHXr/PbBx/wz7JlAOQJDKTFpEkEPvBAms6PiYnB09MTV1dX3N3dCQ4OVqu2iIiIA8RERLBlwgQAavbqRaFy5QAIDAzkl19+Uf3rIBoYJyIiNjmzdSvzW7W6lWybTAR1706jTz4hII1rZV+9epV27doxatQoDMMAUGUvIiLiIJsnTCAuKorrAQH0nzWLn25PbAqqfx1JCbeIiKRLwo0bbBgzhhW9e3P94kXylypF+3nzeGT4cFzT2IU8NDSU1q1bs2/fPlavXk1YWJiDoxYREcm9Tm7cyLGffiIkNpbZJ04QcfUqs2fPtj7wFsdRl3IREUmzc9u3s+7tt4kODQWg9osv0njIENx9fDCbzWm6xvHjx+nYsSMXL16kWLFiLF68mGLFijkybBERkVwr/vp1No4dy4Hr1/kxJgYXb2+aNm3KrFmz1LKdCZRwi4jIfSXGxPDHxx/z93ffAZAvMJAnJ0yg5IMPpus6e/fupUuXLkRGRlKhQgUWLVpE8eLFHRGyiIiIAH9MnsyGY8f4JTIS74IFad26NZ9//jnu7u7ODi1XUMItIiIAWMxmQnft4uaVK+Tx8yOwXj1cXF0J3b2btSNGEHX2LAA1O3TgkWHD8PD1Tdf1f//9d3r27ElMTAxBQUHMnz+fQoUKOeKtiIiICHBuxw4+/+orNl+9ilfBgvTs1Ytx48bh4qKRxZlFCbeIiBCydi2bJ07kxr/GUvv6++NXtSqntmwBw8A3IIAnx4+ndOPGNt0jJiaGuLg4HnnkEWbPno1vOhN2ERERSbukuDjWjx5NrNmMu7c3w996i8GDB6sbeSZTwi0iksuFrF3LmoED4T8Tp9y4dIkbly4BUP3552ny1lt45s1r832eeuopFi1aRIMGDfDw8MhQzCIiInJv26dNI+rsWZ6vVo2hI0bQvEULZ4eUK2XZvgTx8fG8+eabFC9eHG9vbxo2bMi6devSdO769et57LHHKFKkCAUKFKBBgwbMmzfPwRGLiGQ/FrOZzRMn3pVs/5t3wYI0e++9dCfbhmEwc+ZMzp07Z93WuHFjJdtZnOpfEZHsLSYmhneHDWPn7NkAPDl2rJJtJ8qyLdw9evRg6dKlDBo0iIoVKxIcHEyLFi3YtGkTje/RnXH16tW0adOGhx56iHfffReTycSSJUvo1q0b4eHhvPHGG5n4LkQkN0ht7HNWYE5IICYigpvh4cRcuXLrv+Hh1tdXT526oxt5SmIjIwndtYuSDRum+b4Wi4WxY8cyc+ZMgoODWbduHT4+Phl9O5IJVP+KiGRf165do8uLL7J1wwZq+fjwZo8elHv8cWeHlatlyYR7586dLFq0iEmTJjF06FAAunXrRo0aNRg+fDjbtm1L9dypU6dSrFgxNm7ciOft9WD79u1LlSpVCA4OVoUvInaV4tjngACajhxJhebNHXJPw2Ih9tq1FBPo/76Oi4qyyz1vXrmS5mMTExMZNmwYy5YtA6Bnz55KtrMJ1b8iItlXWFgYnTp14uDevXgYBo2KF6fp2287O6xcL0sm3EuXLsXV1ZU+ffpYt3l5edG7d29GjhzJuXPnKFmyZIrnRkdHU7BgQWtlD+Dm5kaRIkUcHreI5C73Gvu8ZuBAnv3sszQn3YZhkHDz5p1J85Ur/588/zuRjojASOOa1wAubm74FClCniJFbv3Xz8/6OjYyku1Tp973Gnn8/NJ0r7i4OF5++WU2bNiAq6srU6ZM4fnnn09zrOJcqn9FRLKnU6dO0blzZ86eOYNnfDzdAgPpOmECPoULOzu0XC9LJtx79+6lUqVK5MuX747tDRo0AGDfvn2pVvhNmzblww8/ZPTo0XTv3h2TycSCBQvYtWsXS5YscXjsIpI73HPss2GAycTmiRMp9cgjxEVG3rMVOvl1UlxcumLwLlQoxSTaJ3nb7f965c+PKZXlPyxmMweXLr01OVpK78Vkwtffn8B69e4bT3R0NEOGDOHw4cN4eXkxY8YMnnjiiXS9J3Eu1b8iItnPsWPHePvtt4mIiCC/xULH4sUJevxxqrRq5ezQhCyacF+8eJFixYrdtT1524ULF1I9d/To0Zw6dYoJEyYwfvx4AHx8fFi2bBmtW7e+533j4+OJj4+3vo6OjgbAbDZjTqE1yWw2Y7FYUtwnaaMyzDiVYcbZUobnd+6899hnw+BGWBhf1qmTrljc8+S5I2n+b/Kc/ONdsCCu7u5puqbFMOAe7+2Rt97i5zfeAJPpzqT79rIhj7z1Fgbct3xGjRrFgQMHKFiwIN9++y3169fX5zKd0vJZdHXg/ADOqH9V9zqHyjHjVIYZpzLMuNjYWEaOHMnVq1cpXbgwz8TGUjBvXpq+8w4Wi8XZ4WULaf0c2lr/ZsmEOzY29o4uacm8vLys+1Pj6elJpUqVaNeuHW3btsVsNjNjxgy6dOnCunXrePDBB1M99/3332fs2LF3bT9x4kSK68VaLBauXr1KSEiIFo+3kcow41SGGWdLGV44cCDN1ze5ueFZsCCeBQrgWbAgHrf/m9Jr1xT+9iVLBKKAqKgosNPYbADKlCFo+HAOz5pFfESEdbNX4cJU6d0bo0wZjh8/ft/LdO7cmX/++YchQ4ZQoECBNJ0jd0rLZ7FKlSoOu78z6l/Vvc6hcsw4lWHGqQwzzmKx8Prrr7Nq2TIeDwvD3c2NCl26cOnmTS6pHk6TtH4Oba1/TYZxj7VgnKRGjRr4+/uzYcOGO7YfOnSI6tWrM336dPr27Zviua+88grbt29nz5491gJLTEykevXqFCxYkB07dqR635SespcsWZKrV6/e1b0Obj0NCQkJoUKFCg5tccjJVIYZpzLMOFvK8PzOnazo2fO+xz0zdSplmzbFdLu1OCuzmM1c2L2bmCtX8PHzo3jduvedbT0yMpKCBQsCt8rx+PHjVKxYUZ9FG6Xls+jIsnVG/au61zlUjhmnMsw4laHtkuvf5Lr3yKefcu6PPyhWpw7Pz52b6lAyuVtaP4c5qoW7WLFihIaG3rX94sWLABQvXjzF8xISEpg9ezbDhw+/4+mEu7s7//vf/5g6dSoJCQmprgHr6emZ4pN9V1fXVAvYxcXlnvvl/lSGGacyzLj0lmHJBg3wDQi479jn8o89lmWWCLsfV1dXSj/0UJqP37FjB927d+fdd9+lY8eO1mvos5gxzvx9dkb9q7rXeVSOGacyzDiVYfp9+eWXTJ06leXLl1OxYkUu/fEH5/74A1d3d56cMAG3NA45k//nyM9hlnz0ERQUxLFjx6zjuJIlPx0PCgpK8byIiAiSkpJS7H+fmJioMSIiYjcurq40HTky1WQboOnIkdkm2U6vdevW0bFjR6Kjo1m6dKnGieUQqn9FRLIuwzAYP34848eP59q1a6xbt47Yq1c5PGsWAA379aNQuXJOjlL+K0sm3O3atbOO/UoWHx/PnDlzaNiwoXWG1LNnz3LkyBHrMUWLFqVAgQKsWLGChIQE6/YbN27www8/UKVKFby9vTPvjYjkEBazmXM7dnBkzRrO7diBRV+cASj72GP4pLBclq+/f7qWBMtuvv/+e3r16kV8fDxPPvkk8+fP19i7HEL1r4hkdzn1O0tSUhJDhgzhyy+/BG5NVNq/f39+e/99Eq9fp3ClStTt3dvJUUpKsmSX8oYNG9K+fXtGjBjB5cuXqVChAt9++y2nT59m9uzZ1uO6devGli1bSB6G7urqytChQxk1ahQPPvgg3bp1w2w2M3v2bM6fP8/8+fOd9ZZEsq2QtWvZPHHiHTNy+wYE0HTkyBybUKbVwaVLiblyBa+CBXnq/feJv3GDPH5+BNarl2NbtmfOnMmYMWMAaN++PZMnT8ZdXddyDNW/IpKd5dTvLPHx8bz66qv88ssvuLi4MHnyZDp27MjJTZs49tNP4OJCs3Hj0rx6iWSuLJlwA8ydO5fRo0czb948IiMjqVWrFmvWrKFJkyb3PO/tt9+mbNmyfPbZZ4wdO5b4+Hhq1arF0qVLef755zMpepGcIWTtWtYMHHhXt+kbly6xZuDAHN2Kez8JN2+yY9o0AB7s14+yTZs6NyAHMwyDjz76iM8++wyAvn37Mnr0aLVs50Cqf0UkO8qp31muX79Oz5492bZtGx4eHkyfPp2nn36a+Bs32Hh7hYcyrVrhX6OGkyOV1GTZhNvLy4tJkyYxadKkVI/ZvHlzits7d+5M586dHRSZSO5gMZvZPHFiymOUDQNMJjZPnEi5Zs1ybGvuvewJDiYmPJz8pUpR84UXnB2Ow5lMJtzcblUZI0eOpF+/ftli5nVJP9W/IpLd5OTvLB4eHhiGga+vL8HBwTRq1AiArR9/zI2wMPKXLEnFTp2cHKXcS5ZNuEXEuUJ37bqjS9ZdDIMbYWGE7tpFyYYNMy+wLCAmIoLdt7vXNho0CNdUVj7IaQYPHkyTJk2oX7++s0MRERGxysnfWTw9PQkODub8+fNUrVoVgPN//cX+hQsBeHzsWGJTWOlBsg71BRSRFN28csWux+UkO7/6isSYGIpWr06lp592djgOc+PGDcaOHUtMTAxwq5VbybaIiGQ1Oe07y5EjR5g6dar1dd68ea3JdlJcHOtHjQKgRvv2lMhmDxByI7Vwi0iK8qQw+3ZGjssprp09y/7FiwF4ZNgwTDl0DHNERARdunTh77//JjQ09I5Zq0VERLKSnPSdZffu3XTp0oWoqCgKFy5Mp/90F98+bRrXzpwhT9GiNB42zElRSnrkzG+KIpJhgfXq4RsQYF1T+i4mE74BAQTWq5e5gTnZtilTsCQmUrpxY0o++KCzw3GI0NBQ2rRpw99//02hQoV47bXXnB2SiIhIqqzfWe4j9K+/MCcmZkJEttm0aRMvvPACUVFR1K1bl//973937L986BC7v/kGgMfHjMErXz5nhCnppIRbRFLk4upK05Ej73lM05Ejs93kIxlx6eDBW8tvmEw8PGSIs8NxiOPHj9OqVStOnDhB8eLFWblyJUFBQc4OS0REJFVp+c4CsH3qVBZ37EjE8eOZEFX6rFy5ku7duxMbG8tjjz3G4sWLKVCggHW/OTGRdW+/jWE2U/F//6N8s2bOC1bSRQm3iKSqQvPmPPzGGynuq/3ii9lyeQ1bGYbBHx9/DECVli0penssVU6yZ88eWrduzcWLF6lYsSKrV6+mQoUKzg5LRETkvio0b57iEp2+AQE889lnPD1pEp7583P5n39Y0LYtf82ciSUpKfMDTcGcOXPo168fSUlJtGnThuDgYHx8fO44Zs+cOVw5fBjP/Plp+vbbTopUbKEx3CJyT67u7gAUCwqidpcunN+5k4NLlnB261YsZnOuaeE+u3Ur5/78E1d3dx4aMMDZ4dhdYmIir732GteuXaNOnTrMnz+fggULOjssERGRNIuNjASgft++FK5YkTx+fgTWq2f9rlKiQQM2jBnDqc2b2frxx5xYv57m779PoXLlnBbzkSNHGDVqFIZh0LNnT8aNG4fLf+aHiTx1iu23J1F7dMQI8hQp4oxQxUZq4RaRe7qwZw8A5Zo1o8qzz9Jk+HA88+cn8tQpQtaudXJ0mcOwWKyt27U6dyZ/iRJOjsj+3N3dmTFjBi1atGDJkiVKtkVEJFtJiovj8qFDwK3Zu6s8+ywlGza8o2HA19+fVl99RfP338cjb17C/v6b79q0Yfc332Axm50Sd5UqVRg3bhxDhgxh/PjxdyXbhsXC+tGjMSckUOrhh6naurVT4hTbKeEWkVQZhsGFffsAKF6nDgAevr7U6dYNuLU8lmGxOCu8THP0xx+5cvgwHr6+NHjlFWeHY1dh/1q3tFatWsyaNYs8efI4MSIREZH0u3TwIJbERHz8/MgXGJjqcSaTiWrPPUfXH36gdOPGmBMS+P2jj1jatSuRp09nSqyJiYmEh4dbX/fq1YshQ4ZgSmGi2gNLlhC6axfuPj40Gzs2xWMka1PCLSKpig4NJebKFVzc3fGvUcO6PahLFzzy5CH82DFObt7svAAzQVJCAtumTAGg3ksv4Z1DWn4Nw2DKlCk0btyYPbd7MYiIiGRXF/buBaB4UFCaktK8AQG0mTmTZu+9h7uPDxf27OG7Nm3YN2+eQxsTYmJi6N69u3U28nu5HhbGH5MmAdBo0KAc2cMuN1DCLSKpSu5OXrRaNdy8vKzbvfLnp/aLLwKw88svMQzDKfFlhgOLFhEdGkoePz9ry352Z7FYGDNmDB999BExMTFs27bN2SGJiIhkyMXb31mKP/BAms8xmUzUfOEFuv7wAyUffJCkuDg2T5jAsh49iDp/3u4xXrt2jQ4dOrB582bOnDnD0aNHUz3WMAw2jhlDws2bt+bRuf29S7IfJdwikqqLt58WF0thWag6PXrg5uXFpYMHOfPHH5kcWeaIv36dHV9+CcCD/fvj/p8ZQ7OjxMREBg4cyKxZswB47733eP31150clYiIiO0Mw7C2cBe7PQQuPfIFBtL2m2947J13cPP25vzOncxv1Yr9ixbZrVEhLCyM5557jt27d5M/f36WLFlCgwYNUj3+6I8/cmrLFlzd3Xli/PhcM0ltTqSEW0RSZe2elULl5VOoEDU7dABuj+XOga3cu2fPJu7aNQqWLUv1tm2dHU6GxcbG0rt3b5YtW4arqytffPEFL730krPDEhERyZBrp08Td+0arh4e+FWrZtM1TC4u1O7cmS6rVhFYrx6JMTFsfPddVvTuTfSFCxmK7+TJk7Rq1YqjR4/i7+/PypUrqVu3bqrHx0ZGsmXCBAAavPoqhbVEZ7amhFtEUhR/4wYRx44BqT8trturF64eHlzYs4fQv/7KzPAc7ubly+wJDgbg4cGDcXHL3qsoXr9+nY4dO7J+/Xq8vLyYM2cOzz//vLPDEhERybDkIXD+NWvi5uGRoWsVKFWKdnPn0mTECFw9PTm7bRvzW7Xin2XLbGpcOHToEK1bt+b8+fOULVuW1atXU7ly5Xues2XiRGIjIylSqRL19GA821PCLSIpCvv7bwyLhXyBgfj6+6d4jK+/P9XbtQNg5/TpmRmew22fNo2kuDiKBQVR/oknnB1Ohnl7e1OwYEHy5cvH4sWLeSIHvCcRERGAi7dXVLGlO3lKTC4uPNC9O11WrqRYUBAJN26w7u23WfXKK9y4dCld1ypYsCBeXl7UqFGDVatWUbJkyXsef2rLFo788AMmFxeemDAB1ww+QBDnU8ItIim6mMaxUPV698bFzY2z27ZZK7zs7urJkxxcuhSAxkOH5oglONzc3Jg+fTpr1qyhfv36zg5HRETEbu41BC4jCpYtS/vvvuORYcNw9fDg9JYtzGvZksOrVqW5tbtYsWJ8//33LFu2jCJFitzz2PgbN9j47rsA1OnenYCaNTP6FiQLUMItIim6kMbZPvMFBlK1dWsg57Ryb/v0UwyzmXKPPUZgvXrODsdmhw4dYsKECdYvBV5eXlTQODAREclB4q5d42pICGC/Fu5/c3F1pW7v3nRevhz/mjWJj47m1zff5Id+/bh55UqK5yxevJjVq1dbX5cpU4a8efPe915bP/mE6xcvkr9kSR4aMMBu70GcSwm3iNzFYjYT9vffQNoqr3ovv4zJxYVTmzdz+dAhR4fnUBf37SNk3TpMLi48PHiws8Ox2Y4dO2jbti3Tpk3jm2++cXY4IiIiDnHx9veVAqVL41OokMPuU7hCBTosXEijQYNwcXfn5MaNzHv2WY7++OMdrd1ffvklb7zxBv379+fw4cNpvn7orl3sX7AAgCfGjcPd29vu70GcQwm3iNwlIiSEhJs3cffxoUjFivc9vmCZMlRq0QLI3q3chmHwx+TJAFR77jkKp+G9Z0Xr1q2jY8eOREdH07BhQ9q3b+/skERERBwirUPg7MHFzY0Gr7xC56VL8atWjbioKH4eMoQfBw7kZkQE48ePZ/z48QC8/PLLVKlSJU3XTYqPZ/2oUQBUb9eOkg8+6LD3IJnPpoT7wQcf5LvvviMxMdHe8YhIFpDcnbxYUFCaZ+du0LcvACFr1xJx/LjDYnOk01u2ELprF66enjzYv7+zw7HJ999/T69evYiPj+fJJ59k4cKF5MuXz9lhiYiIOERah8DZU5HKlem4eDEPvv46Lm5uHP31V1rVrMkXn34KwOjRoxk1alSa54DZ8eWXRJ4+TR4/Px4ZPtyRoYsT2JRw79y5k27dulGyZElGjx5NaGioveMSESeyPi0OCkrzOYUrVqRC8+YA/DVjhiPCcijDbGbb7YqyTrdu5A0IcHJE6TdjxgwGDhyI2WzmhRdeYPbs2Xh5eTk7LBEREYcwJyYSduAAYP8J0+7H1d2dB19/nefmzWNVbCy7Ll8mPjqabg88QI+OHdN8ncuHD7Nr1iwAHnvnHbz0kDzHsSnhXrRoEY0bN+by5ctMnDiRsmXL0r59e7Zs2WLv+ETECWztntXglVcAOPrjj1w7c8bucTlS6ObNXA0JwTN//my55uXJkyet3dj69u3LJ598gls2XztcRETkXsKPHiUpNhbPfPkoVL68U2LYuH8/IUlJ+OTLR8fixQkICWFey5ac2LjxvudakpJY//bbGGYzFZ96igpPPpkJEUtmsynhfuGFF9iyZQsHDhzg5ZdfxtPTk2XLlvH4449Tq1Ytvv76a2JiYuwdq4hkgptXrhB17hyYTOlq4QYoWq0aZR99FMNi4a+ZMx0ToAMkxcURcnuikgZ9+uCVP7+TI0q/cuXKMWXKFEaMGME777yDi4um6BARkZzt30PgTE6q97p27UrPnj1ZtmYN765ZQ6Hy5YkJD+eH117j1zffJC4qKtVz9wQHc/nQITzz56fp7THckvNk6JNZvXp1pk+fTmhoKFOmTKFixYocPHiQ1157jcDAQN544w2OZ9OxnCK5VXLrdpGKFfFMwxIW/1X/div34ZUric4mw032L1hAXEQEvgEB1O7SxdnhpFlCQgIXLlywvm7bti39+/fPEeuGi4iI3M/FffuAzJkw7d8uXLhAfHw8AC4uLkyYMIFGjRoRULMmnZcvp27v3mAycXjVKua1bMmp334Dbq0Cc27HDo6sWcPhVavY9vnnADR5803y+Pll6nuQzGOXR0H58uVjwIABHDlyhLVr19KyZUuioqL4/PPPqVq1Ki1atGDDhg32uJWIONiFDM72WbxOHUo+9BCWpCR2zZ5tz9AcIu7aNXbdbo1/sH9/3Dw9nRxR2ty4cYMuXbrQtm1bLl265OxwREREMl1yI0Fmjt8+cuQIzzzzDP369cNsNt+1383Tk0eGDeOF776jQOnS3Lx8mVV9+rCsZ09mP/YYy7p355ehQ/n1zTexJCRQpHJlqj33XKbFL5nPrn0vbt68yfHjxwm5vfi8YRiYTCZ++eUXmjdvzrPPPkvUPbpViIjz2WO2z+Sx3P8sXcqNLJ4M/jVzJvHR0fiWLk3lli2dHU6aRERE0K5dO/744w8iIiI4k83Gy4uIiGTU9YsXuX7xIiZXV/xr1syUe+7atYs2bdpw6dIlTpw4cc+8pvgDD/DiypXU6d4dgHN//snNy5fvOi786FFOrFvnsJjF+eyScB87dowBAwYQGBjI66+/zqFDh3j88cdZuXIl165dY8aMGZQpU4aff/6ZIUOG2OOWIuIASfHxXD50CMhY96wSDRpQ/IEHMCcksGfOHHuFZ3fXL15k37x5AFTu2hUXV1cnR3R/oaGhtG7dmv3791OoUCGWLl1KgwYNnB2WiIhIpkrukedXuTIeefI4/H4bN27khRdeIDo6mrp167JixQoKFSp0z3Pcvb15ZPhwvO91nMnE5okTsaTQWi45g80Jt2EYrFq1iubNm1O1alWmTp1KYmIivXv3Zv/+/axfv55WrVqRJ08eXnrpJfbv30/ZsmX54Ycf7Bm/iNjR5X/+wZKYiE/hwuQvWdLm65hMJhq8+ioA+xcvJubqVXuFaFd/fv455oQEAuvXp0jdus4O576OHTtGy5YtOXnyJMWLF2fVqlXUrl3b2WGJiIhkOmt38kxYf3vFihX06NGDuLg4HnvsMRYvXkyBAgXSdG7orl3E3ut7kGFwIyyM0F277BOsZDk2Jdwffvgh5cqVo23btqxfv54SJUrw/vvvc+7cOWbMmEGNGjXuOidPnjw0btyY8PDwDActIo5hne3zgQcyPPFW6caN8a9Rg6TYWPZ++609wrOr8KNHObRyJQCNBg/O8hONHTx4kDZt2hAWFkbFihVZvXo15Z20BIqIiIizWb+zOHj89vz58+nXrx9JSUm0adOG4OBgfHx80nz+zStX7HqcZD82JdwjRozgzJkzNGrUiCVLlnDy5EnefPPN+3arqFGjBk2aNLEpUBFxvOTuWcXTuRxYSv7dyv33/Pn3XBbDGbZ++ikYBhWfeoqAWrWcHc59FS9enCJFilCnTh1WrlxJ8eLFnR2SiIiIUyTGxHDlyBHA8Ql3pUqV8PT0pGfPnkydOhV3d/d0nZ/W2cc1S3nO5WbLSd26dWPgwIHUSecHfOjQoQwdOtSWW4qIgxmGYe2eZa/Kq9xjj1GkUiXCjx1j3/z5PNivn12um1Hn//qLU5s3Y3J1pdGgQc4OJ00KFSrEkiVLyJs3L3kyYayaiIhIVhV24ACG2YxvQAD5HPwAukGDBmzYsIGyZcva1BsusF49fAMCbk0iaxh3H2Ay4evvT2C9enaIVrIim1q4g4OD051si0jWdu3MGWKvXsXV3Z2iKQwLsYXJxcW6LvfeuXNJuHHDLtfNCMMw+GPyZABqtG9PwbJlnRxR6ubOncv8+fOtrwMCApRsi4hIrmfvBoJ/S0xMZNiwYRy6PYksQLly5Wweeubi6krTkSNvvfjvNW6/bjpyZLaYuFVsY1PC7erqSu/eve973Msvv4ybm02N6CKSyZIrr6I1auDm4WG361Z86ikKlilDfFQU+xctstt1bXVi3TrC/v4bN2/vLNPi/l+GYfDpp5/y1ltv8eabb7J//35nhyQiIpJlXHDQ+tsxMTF0796d7777jm7duhEfH2+X61Zo3pxnP/sMX3//O7b7+vvz7GefUaF5c7vcR7Imm7JhwzAwUuoSkcqxIpL1OarycnF1pX7fvqwdMYI9c+ZQ+8UXcff2tus90sqcmMjWTz4BoG7PnllyvJTFYmHMmDHMnj0bgEGDBlEzk9YXFRERyeoMi4WL+/YB9v3OEhkZSdeuXdmzZw/e3t5MnjwZT09Pu12/QvPmlGvWjNBdu7h55Qp5/PwIrFdPLdu5gEObn2NiYtI9sYCIOIe1e5YDlteo/OyzbJ82jejz5zn4/ffU6dbN7vdIi3+WLSPy9Gm8CxbkgV69nBLDvSQmJvLGG2+wfPlyAMaNG5em3kQiIiK5xdWTJ4mPisLNy4siVarY5ZphYWF07NiRY8eOkT9/fubPn09dBywX6uLqSsmGDe1+XcnabF6H+36uXbvGH3/8QbFixRx1CxGxk7joaCKOHwegmB1mKP8vV3d36r/8MgC7Z88mKSHB7ve4n8SYGLZPmwZAw9dew9PXN9NjuJfY2Fh69erF8uXLcXNzY+rUqUq2RURE/iO5gcC/Zk1c7dCwd/LkSVq1asWxY8fw9/dn5cqVDkm2JfdKcwt3uXLl7ni9dOlSNm/enOKxSUlJhIWFYTab6du3b4YCFBHHC/v7bwDylypFniJFHHKPqs89x44vv+TGpUscWr6cWh07OuQ+qdnz7bfEXLlCvhIlqNmhQ6beOy1Wr17Nhg0b8PLyYubMmTRr1szZIYmIiGQ51iFwduqRN2nSJM6fP0/ZsmVZtGgRJUuWtMt1RZKlOeE+ffq09d8mk4kbN25w4x4zDnt4eNCmTRsmTpyYoQBFxPEu7NkD2H/89r+5eXhQt3dvtkycyK6ZM6n+/PN2eTKdFjFXr7J71iwAGg0ahKsdJ4WzlxdeeIEzZ87w2GOPUb9+fWeHIyIikiVdtPN3lkmTJuHj48OIESMo4qBGB8nd0pxwnzp1Crg1CVq5cuVo164dkyZNSvFYDw8P/Pz8NEO5SDbhyOU1/q1G+/b89fXXRIeGcnTNGqo995xD75ds5/TpJNy8SdFq1ajcokWm3DMtzp49S6FChfD19cVkMjF8+HBnhyQiIpJlxUZGEnm7ETAjQ+AOHz5MlSpVMJlM+Pr68vHHH9snQJEUpHkMd+nSpSldujRlypShe/fuPP3009Zt//0pVqyYkm2RbMKSlETY7WWnHNnCDeDu7c0DPXsC8NfXX2Mxmx16P4Co8+fZv3AhAI2HDsXk4rCpK9Lln3/+oWXLlvTu3ZsEJ4xpFxERyW6SGwgKlS+PV4ECNl1j0aJFPPnkk3zxxRd2jEwkdTZ985wzZw69suAMvyKSfuFHj5IYE4OHry+FK1Z0+P1qdeyIV/78RJ4+zfFff3X4/bZNmYIlMZFSjRpRqlEjh98vLbZv307btm25cuUKERER9xyeIyIiIrdcyGCPvC+//JLBgwdjsVg4c+aMli+WTJE1mnpExGmslVdQUKa0/nr4+lKne3cAdn71FYbF4rB7XT50iKNr1gC3WrezgnXr1tGpUyeuX7/Ogw8+yPLlyylUqJCzwxIREcnyklu409sjzzAMxo0bx/jx4wHo168fkydPxmQy2T1Gkf9KU7/vXr16YTKZmDhxIv7+/ulq3TaZTMyePdvmAEXEsTJr/Pa/1e7Shd3ffEPE8eOc2LiRCk884ZD7/HF7TFblZ5+laLVqDrlHeixZsoQhQ4ZgNptp3rw506dPx8vLy9lhiYiIZHnmhATCDhwA0vedJSkpiWHDhrF48WIARo8ezauvvuqQGEVSkqaEOzg4GJPJxJtvvom/vz/BwcFpvoESbpGszd7La6SFV7581H7xRf76+mt2fvUV5Zs1s/tT5rPbtnF261Zc3N1pNGiQXa9ti7lz5/LWW28Bt2Yknzx5sua6EBERSaPLhw9jjo/HK39+CpYtm6ZzDMOgb9++/Pzzz7i6ujJ58mQ6ZMGlQSVnS9O3vTlz5gBQrFixO16LSPZ2PSyM6xcuYHJxIaBmzUy9d53u3dk7dy6X//mHM3/8QZlHHrHbtQ2LhT8mTwagVqdO5C9Rwm7XtlW9evXIly8fnTt3ZtSoUbhkkcnbREREsoN/98hL60N6k8lE06ZN2bhxI9OnT+epp55yZIgiKUpTwt399njL1F6LSPZ0cd8+AIpUroyHr2+m3tunUCFqdejAnuBgdnz5JaUbN7ZbK/exn3/m8qFDeOTJQ4NXXrHLNTOqWrVqbNy4kWLFimnMmIiISDpdtLFHXteuXWnWrBnFixd3RFgi96UmFpFc7MKePYDjlwNLTd1evXD18ODi3r2c37nTLtc0JySwbcqUW9d/6SV8nDQhWUJCAv3792fnv95X8eLFlWyLiIikk2EY1u8s9xu/ffbsWbp3705kZKR1m5JtcSYl3CK5mDMmTPu3PEWLUqNdOwB2Tp9ul2seWLyYqHPn8PHz4wEn9ca5ceMGXbp0YdmyZbz88svExsY6JQ4REZGcIDo0lJtXruDi5oZ/jRqpHnfkyBFat27NunXrrPOmiDhbmrqUz507N0M36datW4bOFxH7S4yN5crhw4DzEm641Qp9YMkSzv35Jxf27s1Qa3v8jRvs+PJLAB7s1w93Hx97hZlmERERvPjii+zfv588efIwbdo0vL29Mz0OERGRnCK5gcCvWjXcU6lTd+3aRZcuXYiOjqZKlSq89957mRmiSKrSlHD36NEjQ90glXCLZD2XDhzAkpREnqJFyRcY6LQ48hUvTtU2bfhn6VJ2Tp9Om6+/tvlae775htjISAqWKUP155+3Y5Rpc/78eTp27MjJkycpVKgQ3333HbVr1870OERERHKSC/dZf3vjxo289NJLxMXFUa9ePebNm0f+/PkzM0SRVKUp4e7WrZvGHYrkMP+uvJz9+13/5Zc5tHw5p7ds4fI//1C0evV0X+PmlSvsvr2CwsODB+Pq7m7vMO/p2LFjdOzYkbCwMAIDA1m0aBHly5fP1BhERERyonsNgVuxYgUDBw4kKSmJxx9/nBkzZuDjhB5uIqlJ8zrcIpKzOHv89r8VKF2ays88w5EffmDn9Ok8+8UX6b7Gji+/JCk2loDatSn/5JMOiPLevv76a8LCwqhYsSKLFi2yLqMoIiIitku4cYPwo0eBu1u44+PjmTRpEklJSTz33HNMmTIF90x+4C5yP5o0TSQXMiwWm5fXcJT6ffsCELJuHRHHj6fr3MhTpziwZAkAjYcOdUqL/cSJE+nTpw8rV65Usi0iImInYfv3Y1gs5C1eHF9//zv2eXp6smDBAgYNGsQXX3yhZFuyJCXcIrlQ5OnTxEVF4erpiV+VKs4OB4DCFSpQoXlzAHamcxz3tilTMMxmyjZtSon69R0RXop2796NxWIBblX67777LgULFsy0+4uIiOR0/x2/bTab2XN7iTCAMmXKMHz4cFxclNZI1pSmT+bZs2c5e/YsZrP5jtdp/RGRrCW5dTugZk1cPTycHM3/a/DqqwAc++knIk+fTtM5F//+m+O//gomEw8PHuzA6O707bff0qpVK8aPH49hGJl2XxERkdzk3z3yEhMT6devH23atGHDhg1OjkwkbdI0hrtMmTK4uLhw6NAhKlWqRJkyZdLcZdNkMpGUlJShIEXEvi7cfjKcFcZv/1vRqlUp27QppzZv5q8ZM2g+ceI9jzcMgz8mTwagWps2FKlUyeExGobBlClTmDRpEgCxsbEYhuH0iedERERyGovZzMV9+wDIX6UK3bp1Y8uWLbi7u3Pz5k3nBieSRmlKuEuVKoXJZLKOi0h+LSLZ0/2W13CmBq+8wqnNmzmyejUNX3uN/CVKpHrs6d9+I/Svv3D18OChAQMcHpvFYuGdd97hm2++AWDw4MEMGTJEfw9FREQc4GpICAk3bpDo4UG/d95h7969eHt7M3v2bJo2bers8ETSJE0J9+n/dO3872sRyT5iIyOJPHkSyHot3ADFgoIo1agRZ7dtY/fs2Tw+ZkyKx1nMZrZ+/DEAQV27ktfBE5UlJiYyaNAgVqxYAcC4cePo3bu3Q+8pIiKSm13Yu5eopCSWXLtGZHg4BQoUYN68edStW9fZoYmkmWYXEMllkrtmFSxbFu8sOsFXg1deAeCfpUu5celSisccWb2a8GPH8MyXj/ovv+zQeAzDoG/fvqxYsQI3NzemTp2qZFtERMTBjmzdyuzQUC7HxhIQEMDKlSuVbEu2o4RbJJfJSutvpyawfn2K162LOTGR3be7b/9bUnw8f37+OXBrOTGvAgUcGo/JZKJNmzb4+PgwZ84c2rZt69D7iYiICNw4coTy3t6UKV2a1atXUykT5moRsbc0dSlPTWJiIsuWLWPz5s2cP38ewzAoUaIETZs25fnnn8cjC81+LCK3ZOXx28lMJhMNXnmFlS+/zIHFi6nfty8+hQpZ9//93Xdcv3gR34AAgl58MVNiatWqFQ8//DCFCxfOlPuJiIjkZjfDw4k+d46WRYvSec0aAu4xp4tIVmZzC/eOHTuoXLkyL774IjNmzOCnn37i559/ZubMmXTp0oXKlSvz559/2hxYfHw8b775JsWLF8fb25uGDRuybt26NJ+/ePFiHnroIfLkyUOBAgVo1KgRGzdutDkekZzAnJjIpQMHgFvLa2RlpRs3xr9mTZLi4tgzZ451e1xUlHWd7of698fNy8sh9z99+jTt2rXj4sWL1m1KtiU3UP0rIs70888/88orr3Bu1y4A/CpWJKBUKSdHJWI7m1q4jx49ypNPPsmNGzcIDAykQ4cOlC1bFrj1JXXx4sWcOXOGp556ip07d1KlSpV036NHjx4sXbqUQYMGUbFiRYKDg2nRogWbNm2icePG9zz33Xff5b333qNdu3b06NGDxMREDh48SGhoqC1vVyTHuHLkCElxcXjmz0/B27+zWZXJZKLBq6/yw2uvsW/+fIoFBZEYG8vJTZuIj4qicMWKVG3TxiH3PnjwIJ07dyY8PJwRI0YQHBzskPuIZEWqf0XEWRYuXMiwYcOwWCwUunqVomTtIXAiaWFTwv3OO+9w48YNBgwYwEcffXRX1/GJEycyfPhwPvvsM8aMGcPixYvTdf2dO3eyaNEiJk2axNChQwHo1q0bNWrUYPjw4Wzbti3Vc7dv3857773Hxx9/zBtvvJH+NyeSg1nHbwcFYXLJ+lM4lGvalLzFi3P9wgV+6Nfvjn1lH3sMF1dXu99z+/btdO/enevXr1O9enXretsiuYHqXxFxBsMwmDZtGhMmTACgc+fO1Dx3jktk/R55Ivdj0zfujRs3Ur58eT799NMUx2m7u7vzySefUL58eZu6kS1duhRXV1f69Olj3ebl5UXv3r35888/OXfuXKrnTpkyhYCAAAYOHIhhGNy4cSPd9xfJqS7s2QNk7fHb/3Zi/XquX7iQ4r5dM2cSsnatXe+3du1aOnXqxPXr13nwwQdZtmwZfn5+dr2HSFam+ldEMpthGIwfP96abL/++uu8P3484YcOAdnnO4tIamxKuG/evEmDBg0wmUypHmMymahfvz43b95M9/X37t1LpUqVyJcv3x3bGzRoAMC+28sapWTDhg3Ur1+fzz//HD8/P/LmzUuxYsWYOnVquuMQyUkMw7Am3Nmhe5bFbGbzxIn3PGbzxIlYzGa73O/nn3+mT58+xMfH07x5cxYsWHDX3yCRnE71r4hkpqSkJD744ANmzJgB3OpFO3LkSK4cOoQ5MRHvQoXIr/Hbks3Z1KW8XLlyhIeH3/e4iIgI69ju9Lh48SLFihW7a3vytguptHhFRkYSHh7O1q1b2bhxI2PGjKFUqVLMmTOH/v374+7uTt++fVO9b3x8PPHx8dbX0dHRAJjNZswpfKk3m81YLJYU90naqAwzLq1leP3CBW5evozJ1RW/atWyfJmf37mTG2FhqR9gGNwIC+Pczp2UuJ0M2CouLo7FixdjNptp3749H330EW5ublm+jLIa/T5nXFrK0NUBQymSOaP+Vd3rHCrHjFMZZtyxY8fYtGkTrq6uTJo0ifbt22M2mwn9VwOBxWJxcpRZmz6HGZfWMrS1/rUp4e7WrRvvvPMO//zzD9WrV0/xmH/++YfNmzczduzYdF8/NjYWT0/Pu7Z73Z6NODY2NsXzkruvRUREsGjRIjp06ABAu3btqFmzJuPHj79nwv3++++nGO+JEyfw9fW9a7vFYuHq1auEhITgkg3Gw2ZFKsOMS2sZXvjtNwDyli3L6WwwgdGF27Op38+JAweILVgwQ/eyWCyMHDmSHTt20KVLF06dOpWh6+VW+n3OuLSUoS0TkaaVM+pf1b3OoXLMOJVhxrm6ujJs2DC8vLwICgri+PHjABz//XcA3EuUsG6TlOlzmHFpLUNb61+bEu4hQ4awc+dOHn/8cUaPHk23bt2s3c+io6OZN28e48aNo2XLlgwfPjzd1/f29r7jaXeyuLg46/7UzoNbY8jbtWtn3e7i4kKHDh0YM2YMZ8+epVQqXVNGjBjB4MGDra+jo6MpWbIk5cuXT7FrqdlsJiQkhAoVKji0xSEnUxlmXFrL8MKSJQCUffBBKlasmFnh2cw7MpL9aTiufM2alLDh/SQlJbF9+3YaN25sfaL59NNP63OYAfp9zjhnl6Ez6l/Vvc6hcsw4laFtrly5Qnh4OFWrVrXWv/8uQ8Mw2BISAkCNJ56geDb4zuJM+hxmnKPLME0Jd7ly5VLcfuXKFQYOHMjAgQMpUKAAANeuXbPu3717NxUrVuTEiRPpCqpYsWIpLiGSvB5u8eLFUzyvUKFCeHl5UaBAgbsKq2jRosCtbm+pJdyenp4pPtl3dXVNtfBdXFzuuV/uT2WYcWkpw7DbYy8D69bNFmVdskEDfAMCuHHpEhjG3QeYTPj6+1OyQYN0z1YeHx/Pa6+9xi+//MK0adNo2bKlPod2onLMOGeWoTPqX9W9zqNyzDiVYfqcPXuWjh07cv36dVatWkXp0qXvKsNrZ88SGxGBq7s7xWrVUtmmgT6HGefIMkxTv4PTp0+n+AO3nkIZhkFkZCSRkZHW14ZhcObMGetx6REUFMSxY8es47iS7dixw7o/xTfj4kJQUBBXrlwhISHhjn3J484047DkRgk3b3Ll6FEge0yYBuDi6krTkSNvvfjvBI23XzcdOTLdyfb169fp0qULP//8M25ubimutCCSW6n+FRFHOXLkCK1bt+b06dP4+Pik2nU3eYLXotWr45bCwziR7CZNCfepU6ds/jl58mS6g2rXrh1ms9k6YyHcapGaM2cODRs2pGTJksCtp2RHjhy549wOHTpgNpv59ttvrdvi4uL47rvvqFatWqpP50VysksHDmCYzeQtVoy8AQHODifNKjRvzrOffYavv/8d2339/Xn2s8+o0Lx5uq4XHh5O+/bt2bp1K76+vixYsIAWLVrYM2SRbE31r4g4wq5du2jTpg2XLl2iSpUqrF69mjJlyqR47MW9e4Hs00Agcj9p6lJeunRpR8dxh4YNG9K+fXtGjBjB5cuXqVChAt9++y2nT59m9uzZ1uO6devGli1bMP7V3bRv377MmjWLfv36cezYMUqVKsW8efM4c+YMP/zwQ6a+D5Gs4kI2rrwqNG9OuWbNCN21i5tXrpDHz4/AevXS3bJ9/vx5OnbsyMmTJylUqBALFiygVq1aDopaJHtS/Ssi9rZhwwZefvll4uLiqFevHvPmzSN//vypHp+ccBd/4IHMClHEoWyaNC0zzJ07l9GjRzNv3jwiIyOpVasWa9asoUmTJvc8z9vbm40bNzJ8+HC++eYbbt68SVBQED/++CNPPfVUJkUvkrUkd88qng0TbrjVvbxkw4Y2nx8ZGUmrVq0ICwsjMDCQxYsXpzo3hUhup/pXROzljz/+oGfPniQlJfH4448zc+bMVCdfBIiLjib89qzkxVIZwiKS3WTZhNvLy4tJkyYxadKkVI/ZvHlzituLFi1KcHCwYwITyWYMi4WLtydMy44t3PZQsGBB2rZty/r161m4cGGK6wyLyC2qf0XEXh544AFq165N6dKl+fTTT3F3d7/n8WF//w2GQf6SJcmjeR8kh8hQwr1s2TK+//57jh49SnR09B1dy5KZTKZ0z1IuIvZz9cQJEq5fx83bGz8Hrt+bFRmGgen2BGtvv/02AwcOJG/evE6OSkREJOdKzgdMJhM+Pj4sXLjwnpOk/Zu6k0tOZFPCbRgGL7zwAsuXL08xyYZbv2T//rIrIs6R3J08oFYtXNyybKcWu1u9ejULFy4kODgYT09PTCaTkm0REREHMpvNjBo1iiJFijBkyBAAfH1903x+dp5zRiQ1aZql/L9mzpzJsmXLqFWrFr/++itt27bFZDJx9OhR1qxZQ4cOHQAYNWqUTbOUi4j9JFde2XX8ti2Cg4N59dVX2bJlC3PnznV2OCIiIjleYmIi/fr149tvv+WTTz65ayWD+7EkJd3qUo4SbslZbGrumjdvHp6envz8888EBASwYMECACpWrEjFihVp0aIFjz32GK+99hpNmzbN9FnOReT/WZfXyAXdswzD4NNPP2Xy5MkA9OjRg169ejk5KhERkZzt5s2bvPTSS2zZsgV3d3e++OILqqRzGFv48eMkxsTg4etL4QoVHBSpSOazqYX74MGDPPTQQwTcXs83udv4v7uX9+nTh0qVKt1z0hURcayYiAiunTkDQLHatZ0cjWNZLBZGjx5tTbYHDx7MhAkTcE3n8mEiIiKSdpGRkXTo0IEtW7bg7e3N3LlzadWqVbqvczF5CFzt2ule+lMkK7OphTs2NvaOWX49PT0BiI6OvmNdvaCgINatW5fBEEXEVsmzkxeqUAGve6x5md0lJiYyaNAgVqxYAcD48ePVsi0iIuJgFy9epGPHjhw/fpwCBQowb9486tata9O1cuMQOMkdbGrh9vf358qVK9bXRYsWBSAkJOSO465evUpcXFwGwhORjMgtlVdoaCgbN27Ezc2NadOmKdkWERHJBNu3b+f48eMEBASwcuVKm5Nt0AzlknPZ1MJdoUKFOyZDq1+/PoZhMH36dGbOnAnA4cOH2bx5c7rHb4iI/VzMJbN9lilThvnz5xMVFcXjjz/u7HBERERyheeee47Y2FiaNGlCiRIlbL7OjcuXiQ4NxeTign+tWnaMUMT5bGrhbt68OadOneLQoUPW1yVLluSbb76hfv36PP/88zz00EMkJibSrVs3uwYsImmTlJDApQMHgJzZwn3p0iV27dplfV23bl0l2yIiIg62Y8cOwsPDra87d+6coWQbIOx2A0HhSpXwTMcyYiLZgU0t3J06dSIpKYnY2FgAPDw8WLx4MW3atGH37t3s3r0bgNatWzNw4ED7RSsiaXbl0CHMCQl4FyxIgTJlnB2OXZ06dYqOHTsSGRnJ8uXLqVGjhrNDEhERyfF++uknXn31VSpXrsyyZcvImzevXa6bPOeMupNLTmRTwl2qVCnefvvtO7Y9+OCDnDp1it9++42rV69StWpVgoKC7BGjiNjg393Jk1cSyAkOHjxI586dCQ8Pp0yZMndM1CgiIiKOsWDBAoYPH47FYqFUqVJ4eHjY7doXc8mcM5I72ZRwp8bb25unnnrKnpcUERtduL28Rk4av/3nn3/So0cPrl+/To0aNfjuu+/w8/NzdlgiIiI5lmEYfPnll0yYMAGAF198kQ8++MBuy26a4+O5cvgwkLO+s4gks2vCLSJZg2EYXMhh3bN+/fVX+vbtS0JCAg899BBz5swhX758zg5LREQkx7JYLIwfP57p06cD0L9/f9566y279pyLCgnBkpREHj8/8gUG2u26IlmFTZOmJTt06BCvvPIKVapUwdfXlzx58lC5cmVeeeUVDh48aK8YRSSdokNDiblyBRd3d/yrV3d2OBm2detWevfuTUJCAk899RQLFixQsi0iIuJgkyZNsibbY8aMYcSIEXYfpnbtyBEg5w2BE0lmc8I9bdo06tSpw8yZMzl27BgxMTHExsZy/PhxZsyYQd26dfn888/tGauIpFFyd/Ki1arh5uXl5GgyrkGDBjzyyCN06NCBmTNn4unp6eyQREREcrxOnToRGBjIZ599Rt++fR1yj8jbCbfGb0tOZVOX8p9//pn+/ftjMplo27Yt3bt3p2zZsgCcPn2ab7/9luXLl/PGG29QsWJF/ve//9k1aBG5t5ww+YhhGBiGgYuLC+7u7gQHB+Ph4aGn3yIiIg5kNput47NLlSrF77//jpeDHt4bhvH/Ldw5ZAicyH/Z1ML90UcfYTKZWLRoEd9//z3PPvss1atXp3r16jzzzDMsWbKERYsWYRgGH330kb1jFpH7uPCvGcqzo6SkJIYOHco777yDYRgAeHp6KtkWERFxoCtXrvDss8+ydu1a6zZHJdsA106fJvH6dVw9PChatarD7iPiTDYl3Lt376ZBgwa0b98+1WPatWtHw4YNrWtyi0jmiL9xg4hjx4Ds2cIdHx9P3759WbhwIcHBwfzzzz/ODklERCTHO3v2LK1bt+bvv/9m9OjRJCQkOPyeyetvF61RA1c7LjMmkpXYlHCbTCbKly9/3+PKly+vFimRTBb2998YFgv5SpQgT9Gizg4nXa5fv06XLl34+eefcXd3Z+bMmdSoUcPZYYmIiORohw8fpnXr1pw+fZpSpUqxePFiu66znZqL2bxHnkha2DSGu1atWhw/fvy+xx0/fpyaNWvacgsRsVF2Hb8dHh7Oiy++yIEDB/D19WXOnDk8/PDDzg5LREQkR9u1axddunQhOjqaqlWrsmDBAvz9/TPl3taEOygoU+4n4gw2tXAPHjyYv/76i0WLFqV6zOLFi/nrr7944403bA5ORNIveYby7PS0+Pz587Rp04YDBw5QuHBhli1bpmRbRETEwTZs2MALL7xAdHQ09evXZ/ny5ZmWbMddu0bkyZOAEm7J2dLUwn327Nk7XtetW5c33niDLl26sHTpUrp162adpfzUqVPMmzePFStW8MYbb1C/fn37Ry0iKbKYzYT9/TcAxbPRbJ+HDh3i9OnTlChRgkWLFlGuXDlnhyQiIpLjbdq0ibi4OJo1a8aMGTPw9vbOtHtfvP19xad4cbwLFcq0+4pktjQl3GXKlElxLLZhGKxYsYIVK1akuG/KlCl89tlnJCUlZTxSEbmviJAQEm7exN3Hh8IVKzo7nDRr3rw5X331FfXr1ycgIMDZ4YiIiOQKY8eOpWLFinTu3Bl3d/dMvXdyj7yCVapk6n1FMluaEu5SpUpp8jORbMDanTwoCJfba2hmVb///jtly5alRIkSALRs2dLJEYmIiORshmGwbNkyWrdujbu7O66urnTv3t0psSSP3y6ghFtyuDQl3KdPn3ZwGCJiD9llts9Vq1YxYMAASpYsyQ8//EDBggWdHZKIiEiOZjabefvtt5k7dy6bN2/miy++cFqDmjkxkbADBwAoqPW3JYezadI0EcmassMM5cHBwbz22mskJiZSs2ZNfH19nR2SiIhIjpaQkEC/fv2YO3cuJpOJ+vXrO7X3aviRIyTFxuKZLx95AgOdFodIZrBpWTARyXpuXrlC1LlzYDIRULu2s8O5i2EYfPLJJ3z88ccA9OzZk3HjxuHioud+IiIijnLz5k169+7Nb7/9hru7O1988QWtWrVyakwXbjcQBNSujUnfAySHy1DCHRERwcyZM9m0aROhoaEABAYG8vjjj/PSSy9RuHBhuwQpIvd3cd8+AIpUqoRn3rzODeY/LBYLo0ePZs6cOQAMGTKEwYMHa24IERERB4qMjKRLly7s3bsXHx8fvvnmG5o0aeLssKzfWbL6EDgRe7A54V67di2dOnXi2rVrGIZh3X7o0CHWr1/PpEmTWLBgAc2bN7dLoCJyb2FZuPL6+OOPrcn2hAkT6Nmzp5MjEhERydkMw6Br167s3buXAgUKMH/+fB7IIkuG/nuS11gnxyLiaDb14Th+/Dht27YlMjKSmjVr8umnn7J69WpWr17NlClTqF27NlevXqVt27YcP37c3jGLSAqy8vjt7t27U6FCBaZNm6ZkW0REJBOYTCbeeustypQpw8qVK7NMsn394kVuhIVhcnXFv2ZNZ4cj4nA2tXB/8MEHxMTE8O677/LOO+/ctX/AgAGMGzeOMWPG8OGHHzJr1qwMByoiqTMnJHD5n38AKJ5FKtTExETrmp5FixZlw4YNmb7Gp4iISG7z7/q3cePGbNmyJUvVv8njt/2qVMHdx8fJ0Yg4nk0t3Bs2bKBy5copJtvJRo8eTeXKlVm/fr3NwYlI2kSfOIElKQmfIkXId3tda2cKCwvj6aef5vvvv7duy0qVvYiISE70xx9/0LhxY44dO2bdltXq34u3u5NnxR55Io5gU8IdFhaWpm4pDzzwAGFhYbbcQkTSIfLwYeDW+G1nT0R26tQpWrduzeHDh/noo4+Ii4tzajwiIiK5wU8//cSLL77IuXPn+OKLL5wdTqqSW7iLZZEeeSKOZlPCnSdPHi5fvnzf4y5fvkyePHlsuYWIpMO1I0cA5z8tPnjwIK1bt+bcuXOUKVOGZcuW4eXl5dSYREREcrrvvvuOPn36kJiYSIsWLZg8ebKzQ0pRYkwMV7LIdxaRzGJTwh0UFMRvv/3GgQMHUj1m//79bNmyhaCgIFtjE5E0MAyDa0ePAs6doXzbtm20bduW8PBwqlevzqpVqyhVqpTT4hEREcnpDMNg6tSpDBs2DIvFQufOnfn666/x9PR0dmgpCtu/H8NsxjcggLzFijk7HJFMYVPC/fLLL5OYmMgTTzzBl19+yY0bN6z7bty4wdSpU3nyyScxm8306dPHbsGKyN2izp4lISoKVw8Pilav7pQYfvnlFzp37syNGzd46KGHWLZsGX5+fk6JRUREJDewWCy89957TJw4EYD+/fszadIkXF1dnRxZ6pLX384qE7yKZAabZinv2LEjP//8M/PmzaN///7079+fwoULAxAREQHceuLWrVs3OnToYL9oReQuycuBFa1eHTcPD6fEsH//fhISEnjqqaeYPn16ln2yLiIiklMkJiay9/Z3gDFjxtC3b18nR3R/1vHb6gEruYhNCTfAt99+y0MPPcTkyZM5efIk4eHh1n3ly5dn6NCh2eIXXyS7S35a7MzKa9iwYZQrV442bdrg5mbznxURERFJI09PT+bOncvWrVv53//+5+xw7suwWKyNBBq/LbmJTV3Kk73yyiuEhIRw7tw5tm/fzvbt2zl37hzHjx9Xsi2SSZIrr4BMrLwMw2Du3LnExsYCYDKZaNeunZJtERERB4qOjmbhwoXW1/ny5csWyTbA1ZMniY+Oxs3bmyJVqjg7HJFMY9O343LlylGpUiV++eUXAAIDAwkMDLRrYCJyf3HR0VwNCQEyr4U7KSmJN998k4ULF7JhwwaCg4OdvhSZiIhITnf58mU6d+7MoUOHiIuLo2fPns4OKV2sDQQ1a+KaxdYGF3Ekm1q4L126RKFChewdi4ikU9jffwPgU6wYPrfnUXCk+Ph4+vTpw8KFC3FxcaFFixZKtkVERBzszJkztGnThkOHDuHn50fDhg2dHVK6XdizB3DuiioizmBTC3fp0qWJjo62dywikk7JlVeBTOiadf36dXr27Mm2bdvw8PBg+vTpPP300w6/r4iISG52+PBhOnXqxOXLlylVqhSLFi2iTJkyzg4r3azjtzVDueQyNrVwt2vXjt9++40rV67YOx4RSYfkyquggxPu8PBw2rVrx7Zt2/D19WXBggVKtkVERBzsr7/+4rnnnuPy5ctUrVqVVatWZctkOzYyksjTpwEoVru2c4MRyWQ2JdwjRoygatWqNG/enG3bttk7JhFJA0tSEmH79wOObeE2DINevXpx4MABChcuzLJly2jUqJHD7iciIiK3xmx37NiR6Oho6tevz/Lly/H393d2WDZJXg6sUPnyeBUo4NxgRDKZTV3Kn3nmGVxdXfn777955JFHKFq0KGXKlMHb2/uuY00mExs2bMhwoCJyp/CjR0mMicEjb158S5Z02H1MJhNjx45lyJAhzJo1i3LlyjnsXiIiInJL0aJFeeutt/j999/5+uuvU/yenV2oO7nkZjYl3Js3b7b+2zAMLl26xKVLl1I8VhMqiThG8tPigNq1MblkaIW/FMXGxlor9zp16rB+/XpcHHAfERER+X//rn9ffvllevfune3r3+SEO7NWVBHJSmxKuDdt2mTvOEQknRxZeW3atImBAwcyd+5cgm5fP7tX9iIiIlmZYRhMmjSJX3/9lRUrVpAvXz4g+9e/5oQEwg4cADRDueRONiXcjz76qL3jEJF0Sm7hLlanDnF2vO7KlSsZMGAASUlJzJw5k2nTptnx6iIiIvJfZrOZkSNHMm/ePAB+/fVX2rdv7+So7OPy4cOY4+PxKlCAgmXLOjsckUyXvR+ZieRS18PCuH7hAiYXF/xr1rTbdefMmUO/fv1ISkqidevWTJkyxW7XFhERkbslJCTw2muvMW/ePEwmEx988EGOSbbhX+O369TRUFPJlWxq4f63Cxcu8Ntvv3H+/HkMw6BEiRI0adKEwMBAe8QnIim4uG8fAEWqVMEjT54MX88wDD755BM+/vhjAHr27Mm4ceOyfTc2ERGRrOzmzZv06tWL33//HXd3d7744gtatWrl7LDs6sKePYC6k0vuZXPCHRERwYABA1iyZAkWi+WOfS4uLrRr144vvviCIkWKZDjIrCw8PBzDMLJsYlK5cmVnhyAOkFx5FbfD+G2LxcLo0aOZM2cOAEOGDGHw4MF6Ci0iIuJAV69epWvXruzduxcfHx+++eYbmjRp4uyw7MowDOsQOM1QnrsdPXrU2SGkymKxOPR7r00J97Vr13jkkUesBVenTh3K3h6Tcfr0afbs2cOSJUvYt28ff/75JwW03p6IXVknTLND5WU2mzl37hwmk4nx48fTs2fPDF9TRERE7i0uLo6wsDAKFCjAd999R50c2AIcHRpKzJUruLi54V+jhrPDEXEKmxLu9957jyNHjtCoUSOmT59Ojf/8Av3zzz+8+uqrbN26lffee49PPvnELsGKCCTGxnLl8GHg1niojHJ3d+frr79m+/btPPbYYxm+noiIiNxf8eLFWbRoEQAVK1Z0cjSOkdxA4FetGm5eXk6ORsQ5bOoHvXz5cgoVKsRPP/10V7INUL16dX744QcKFizI8uXLMxykiPy/SwcOYElKIk/RouQtXtyma1y7do2vv/4awzAA8Pb2VrItIiLiYH///Te//PKL9XXFihVzbLIN/7+iij0aCESyK5tauMPCwnj22Wet6wOmJH/+/DRt2pQff/zR5uBE5G7/Hgtly3iTsLAwOnXqxNGjR4mLi2PgwIH2DlFERET+448//qBnz54kJCSwdOlS6tev7+yQHO6iEm4R21q4/f39rS1j92IYBkWLFrXlFiKSiov/Wn87vU6dOkWrVq04evQo/v7+PP300/YOT0RERP7jxx9/5MUXX+TmzZs0bNiQqlWrOjskh0u4cYPw2/M9aYZyyc1sSrifffZZNm/eTFRUVKrHXLt2jU2bNvHMM8/YHJyI3MmwWGx+WnzgwAFatWrF+fPnKVOmDKtXr9Ys9iIiIg723Xff0bdvXxITE2nRogXz58/H19fX2WE5XNj+/RgWC/kCA/H193d2OCJOY1PCPW7cOAoVKsQzzzzDgQMH7tp/8OBBWrZsSeHChRk3blyGgxSRWyJPnyYuKgo3Ly/80vF0fNu2bTz//PNERERQo0YNVq9eTcmSJR0YqYiISO5mGAZffPEFw4YNw2Kx8OKLL/L111/j4eHh7NAyxYUM9MgTyUlsGsM9dOhQatSowapVqwgKCqJ27drWZcFOnTrF/v37MQyDVq1aMWzYsDvONZlMzJ49O+ORi+RCya3b/jVq4OrunqZzIiIi6NatGzExMTz00EMEBweTN29eR4YpkmVl5XVAwfFrgeYU4eHhGIaBi4tN7QaZQj2IZO3atbz//vsADBgwgDfffDNX/X5f2LMH0PhtEZsS7uDgYOsfDMMw2LdvH/v27bvruFWrVt21TQm3iO2slVc61t8uXLgwEyZMYO3atXz55Zd4eno6KjwRERG57cknn+T555+nZs2a9OnTx9nhZCqL2UzY338DUCwd31lEciKbEu45c+bYOw4RSYP0dM+Kjo62riTQoUMHXnjhhVz1ZF1ERCSzxcXF4eLigoeHBy4uLnz++ee5su69GhJCwo0buPv4UCQHL3smkhY2Jdzdu3e3dxx3iY+P55133mHevHlERkZSq1Ytxo8fz5NPPpmu6zz55JOsX7+efv36MXXqVAdFK+J4sZGRRJ48CUCxoKBUjzMMgwkTJvDTTz+xatUq/Pz8AHJlhS8i6af6V8Q20dHRdO/eHX9/f6ZNm4arq2uurXuTGwgCatfGxc2mdEMkx8iyg5969OjBJ598wosvvshnn32Gq6srLVq04I8//kjzNZYvX86ff/7pwChFMs/F28M2CpYrh3fBgikek5SUxJAhQ/jyyy85ffo0mzdvzrwARSRHUP0rkn6XL1+mbdu27Nixg82bN3P69Gmbr2Uxmzm3YwdH1qzh3I4dWMxm+wWaSbT+tsj/y5IJ986dO1m0aBHvv/8+kyZNok+fPmzcuJHSpUszfPjwNF0jLi6OIUOG8Oabbzo4WpHMcb/KKz4+nj59+rBo0SJcXFz49NNPad++fWaGKCLZnOpfkfQ7c+YMbdq04dChQ/j5+bF8+XLKly9v07VC1q7lm2bNWNa9O78MHcqy7t35plkzQtautXPUjqUZykX+X5ZMuJcuXYqrq+sdE0x4eXnRu3dv/vzzT86dO3ffa3z00UdYLBaGDh3qyFBFMs29Kq+bN2/StWtXfvnlFzw8PJg9ezYdOnTI7BBFJJtT/SuSPidOnKBt27acPn2a0qVLs2rVKqpVq2bTtULWrmXNwIHcCAu7Y/uNS5dYM3Bgtkm6b4aHE3X2LJhM9xwCJ5JbZMmEe+/evVSqVMk64VOyBg0aAKQ4I/q/nT17lg8++IAPP/wQb29vR4UpkmnMiYlcur3m/X9nKA8PD2fAgAH8+eef+Pr6snDhQp566ilnhCki2ZzqX5G0++uvv+jfvz+XL1+matWqrFy5kjJlyth0LYvZzOaJE8Ew7t55e9vmiROzRffy5B55RSpWxFPLkIrYNmmao128eJFixYrdtT1524ULF+55/pAhQ6hTpw4dO3ZM133j4+OJj4+3vo6OjgbAbDZjTuEPnNlsxjAMLBZLuu6TmVKKOysxm81YLJYsH6ezXfrnH5Li4vDKn598JUveUV5ms5nY2FgKFy7M/PnzqVGjhsoznfQ5tI/sUI5Z+e81/P863PcqQ1dXV4fd3xn1b06se0H1b26QmJhIQkIC9erV49tvvyVfvnw2l+f5nTvvatm+g2FwIyyMczt3UuL2A7CsKvT2EqYBQUH3LQ99DjMuu5RhVv6bnZa6F2yvf7Nkwh0bG5viWsFeXl7W/anZtGkTy5YtY8eOHem+7/vvv8/YsWPv2n7ixAl8fX3v2m6xWIiNjeXq1atZdhbK48ePOzuEe7JYLFy9epWQkBBcXLJkh4ss4fS6dQDkrViRkBMn7thnsVgYNWoUvr6+eHp6Zvn/51mRPof2kR3KMSIiwtkh3JNhGJhMpnuWYZUqVRx2f2fUvzmx7gXVv7lB4cKFeffdd3nggQe4dOkSly5dsvlaF273YrufEwcOEJvKxKlZxalt2wAwFSt2398DfQ4zLruUYVauf9NS94Lt9W+WTLi9vb3veNqdLC4uzro/JUlJSQwYMICuXbtSv379dN93xIgRDB482Po6OjqakiVLUr58+bu618GtJ0oREREUKlQoy37AK2bxtQ/NZjMhISFUqFDBoa022V3I+fMAVGjcmIoVK7Jr1y5OnTpF+/btrU/jVIa20+fQPrJDORopddfMQpKfsjurDJ1R/+bEuhdU/+ZUwcHBNGzYkKpVq9q1/vWOjGR/Go4rX7MmJbLwZyspPp61t5cwrfP00+QvVeqex+tzmHHZpQyzcv3r6Lo3SybcxYoVIzQ09K7tFy9eBKB48eIpnjd37lyOHj3K119/fddyDNevX+f06dMULVoUHx+fFM/39PRM8cm+q6trqoVvMplwcXHJspV+Vv7FS+bi4nLPMs7tDMOwjocKfOABtmzZwksvvURCQgKBgYE0atRIZWgHKkP7yOrlmFX/Vv+byWRyWhk6o/7NiXUvqP7NaQzD4KOPPuKzzz7D39+fDRs2kD9/fruVYckGDfANCEi9W7nJhK+/PyUbNMAlC///unTkCJbERHwKF6ZgmTJp6oWiz2HGZYcyzMp/r8GxdW+G33lUVBTr169n4cKFbLvdhSSjgoKCOHbsmHUcV7LkbmpBqcx4ePbsWRITE3n44YcpW7as9QdufRkoW7Ysa7PJDI8iya5fvMjNy5dxcXNj+6lT9OjRg7i4OB599FHq1q3r7PBEJAdR/StyN7PZzFtvvcVnn30GQM+ePSlo527dLq6uPNS/f+oHGAZNR47M0sk23LmiSlYe8iGSmWxOuK9fv85LL71E0aJFeeqpp+jSpQuzZs2y7p81axbFixe3aSx1u3btMJvNzJgxw7otPj6eOXPm0LBhQ0qWLAncquCPHDliPaZjx46sWLHirh+AFi1asGLFCho2bGjrWxZxigu3Jx/5x9ubgYMHk5SURJs2bQgODk61t4aIiC1U/4rcKSEhgVdffZV58+ZhMpn48MMPGTBggEOSySuHDwPg4nZ3B9QKzZtToXlzu9/T3pJ75BXX+tsiVjZ1KY+NjaVp06bs3buXokWLUq9ePX766ac7jnn22Wfp27cvK1euTHcl27BhQ9q3b8+IESO4fPkyFSpU4Ntvv+X06dPMnj3bely3bt3YsmWLdUxAlSpVUh3MXrZsWdq0aZO+NyqSBVzYs4dNV6+y9cIFPHx96dmzJ+PGjcvyXXNEJPtR/Svy/27evEmvXr34/fffcXd3Z+rUqbRs2dIh97p29iz7Fy8GoM2MGZhcXbl55QrXzp5l++efc3bbNuKio/FKYV6DrOLfQ+CKKeEWsbIp4f7kk0/Yu3cvnTp1YsaMGeTJk+euL/8BAQFUrVqVTZs22RTY3LlzGT16NPPmzSMyMpJatWqxZs0amjRpYtP1RLKrDevWsfnqVTzz52fIkCEMHjxY3bRExGFU/4rc8uGHH/L777/j4+PDnDlzeOSRRxx2r21TpmBJTKR048aUatTIut2wWDj+889EHD/O3/Pn0/C11xwWQ0ZFnT1LTEQEru7uFK1e3dnhiGQZNiXcixcvJiAggNmzZ1uXCklJpUqV2Llzp02BeXl5MWnSJCZNmpTqMZs3b07TtbLyrHgi95Jw8yZ+ERE8WKAAbUaN4tVBg5wdkojkcKp/RW4ZNmwYISEhDBs2jDoObLG9dPAgx376CUwmGg8desc+k4sLDV55hZ+HDGHvt99Sp1s3PFJYLi8rSB6/XbRGDdxSmAhRJLeyqU/qiRMnaNCgwT2TbQAfHx/Cw8NtCkwkN4uJiSEmJoZLBw6AxcILtWop2RYREXGwf39vzZs3LwsWLHBosg2w9ZNPAKjSsiV+KQzNqPj00xQoXZq4qChrt/OsSOO3RVJmU8Lt6upKYmLifY87f/48efLkseUWIrnWtWvX6NChAy+99BJnd+0CoFgqMwOLiIiIfezbt49HH32UL7/8MtPueeaPPzi7bRuu7u48NGBAise4uLpSv29fAPZ88w1JcXGZFl96JE/yqvHbIneyKeEuX748f//9N0lJSakec+PGDfbv30/VqlVtDk4ktwkLC+O5555j9+7d7N27l32//QZA8QcecHJkIiIiOdfvv/9O+/btiYyMZM2aNWlqWMoow2Lhj48/BqBW587kL1Ei1WOrtGxJ3uLFiYmI4OD33zs8tvSKi44mIiQEUMIt8l82JdytWrXi4sWLjB8/PtVjxo8fT1RUFM8995zNwYnkJidPnqRVq1YcPXoUf39/VixfjunUKUDds0RERBxlzZo1dOnShZs3b9K4cWOWLFmCu7u7w+979McfuXL4MB6+vjR45ZV7Huvq7k79Pn0A2DVrFkkJCQ6PLz3C/v4bDIP8pUqRp0gRZ4cjkqXYlHC/8cYbBAYGMm7cONq0acOCBQsAuHTpEsuXL6djx45MmjSJMmXK8Mp9/oCICOzfv5/WrVtz/vx5ypYty+rVq/FzcyPh+nXcfXwoUrmys0MUERHJcebPn0/fvn1JTEzkmWeeYf78+fhmwqRkSQkJbJsyBYB6L72Ed8GC9z2nWtu25ClalBuXLnH49jr3WYXGb4ukzqaEu0CBAvzyyy/WxKBr166YTCZ++eUX2rdvz5IlSyhVqhQ//PCDxnCL3Meff/5Ju3btiIiIoGbNmqxatYqSJUtax0IF1KqFi5tNCwqIiIhIKr744guGDx+OYRh06dKF6dOn4+HhkSn3PrBoEdGhoeTx86NO9+5pOsfNw4N6L70EwF8zZmDOhG7vaXVB62+LpMqmhBugWrVqHDx4kC+//JJnnnmGqlWrUrlyZZ544gk++eQT/vnnH6pVq2bPWEVypOQn6Y0aNWLp0qUUud0VS5WXiIiI4yTXvwMHDuTDDz/E1dU1U+4bf/06O25PzPZg//64e3un+dwa7dvjXagQ0aGhHP3xR0eFmC6WpKRbXcpRC7dISjLUbObl5cUrr7yibuMiGVCzZk1WrFhBhQoV8PzXupUXlXCLiIg4TM+ePalVqxZ169bN1Pvunj2buGvXKFiuHNXbtk3Xue7e3jzQsydbP/6Yv77+miotW+KSSQ8KUhN+7BiJMTF4+PpSqEIFp8YikhXZ3MItIrabPn06u3fvtr6uXr36Hcl2TEQE186cAbQkmIiIiD3ExsbyzjvvcO3aNeu2zE62b16+zJ7gYAAeHjzYpiFjtTp1wjN/fiJPnSJk7Vo7R5h+1gaCoCCnJ/8iWZESbpFMZBgG48aN47333qNLly5cuXIlxeMu7tsHQOGKFfHKly8TIxQREcl5oqOj6dSpE7NmzaJPnz4YhuGUOLZPm0ZSXBzFgoIo36yZTdfw9PWlTteuAOz86isMi8WeIaabhsCJ3JtNXcrLlSuX5mNNJhMnTpyw5TYiOUpSUhLDhg1j8eLFwK0xY35+fikee+FfT4tFRETEdpcvX6ZTp04cPnyYfPnyMXTo/7V333FNXe8fwD9J2Fs2KIrgVhStiFpXpaK1uHfdu7ZVq+Kso1bUOuqqWkct7lVXXf0WraNarUrdFRduBESG7JWc3x9KfiJDIAkJ8Hm/Xrxa7spzjyFPnnvPOdcfEomk2OOIefAAN/fsAQA0mzhRpRg8+/fH5cBAvLx7Fw9Onixy8a4OWZO8cvw2Ue6KVHA/evTovdtIJBIIIbTygUaka9LS0vD555/jjz/+gEwmw+LFi9GrV688tw/PSl4NGhRXiERERKXO48eP0bt3bzx+/Bj29vbYsWMHatasqZVYzi1dCiGXw611a5RXsSu7kaUl6vXti0vr1uHiTz/BrXVrrXznToyMRMLz55BIpXCsW7fYX5+oJChSwf3w4cNclysUCjx+/BiHDx/Gjz/+iKlTp2Lw4MEqBUhU0sXHx2Pw4ME4f/48DAwMsHbtWrRt2zbP7TPT0xF58yYAds8iIiIqqpCQEPTp0wcvXrxApUqVsGPHDri6umollvCrV3H/2DFIpFJ8OG6cWo5Zf9AgXNm8GZE3b+Lx2bNwbd5cLcctjKzx27bVq8OgGJ5fTlQSFangrlSpUp7rKleujFatWsHb2xt9+vRBy5Yt892eqLRbsWIFzp8/D3Nzc2zcuBFNmjTJd/uoW7cgT0+HsbU1rPi3Q0REVGhCCIwZMwYvXrxAzZo1sX37djg4OGgtlrOLFwMAanXpApuqVdVyXBNra3j06oUrmzbh4k8/oVKzZsV+l5vjt4neT2OTpvXo0QM1a9bE/PnzNfUSRCWCv78/OnTogL1797632Ab+fyyUU/36HJJBRERUBBKJBGvXrkX79u2xf/9+rRXbAPDw1CmEBQdDZmiIxqNHq/XYHwwZApm+Pp5fvoywS5fUeuyCyCq4OX6bKG8anaW8Zs2auKSFP34ibXv+/LlyBlQjIyOsXbsWderUKdC+4UxeRERERRIWFqb8fzc3N/z888+w0OLTPhRyOf5esgQAUH/AAJg7Oqr1+GYODqjdvTsA4OKaNWo99vtkpqYi6tYtALzDTZQfjRbcYWFhSE9P1+RLEOmcf//9Fz4+Ppg3b16h9xVC4PmbR4IxeRERERXczz//jKZNm+LUqVPaDkUp5MABRN+7B0NLSzQcNkwjr/HB0KGQ6unhyblzyseKFoeIGzegyMyEqZ0dLMqXL7bXJSppNFZwb926FefPn0etWrU09RJEOufkyZPo2bMnXr16hX/++QdpaWmF2j/+2TMkR0VBqq8PhwLeESciIirLhBBYuHAhZs6ciYyMDJw7d07bIQF4fQf4/I8/AgAajRwJI0tLjbyOZYUKqNGxI4DXz+UuLsoeeQ0acAgcUT6KNGnakCFD8lyXkJCA27dv49atW5BIJBg7dmyRgyMqSQ4cOIAxY8YgMzMTrVu3xrp162BoaFioY2SNhbKvVQt6hdyXiIiorJHL5Zg2bRq2bNkCAJgyZQpGq3mcdFFd3bYNiRERMHdyQr2+fTX6Wl4jRiDkwAE8PH0aL27dgn0x3PAK54RpRAVSpIJ748aN793GwsICs2fPRr9+/YryEkQlSmBgIKZPnw4hBLp06YJly5ZBX1+/0Mfh+G0iIqKCSU9Px1dffYXDhw9DIpFgwYIFOvO9MzUuDpfWrgUANBkzRuMX0cu5uqJa+/a4c/gwLq5ZA78VKzT6ekIIzlBOVEBFKrgDAwPzXGdgYIDy5cujUaNGMDIyKnJgRCXFsmXLsHDhQgDA4MGDMWfOHEilRRutkTVDuXODBmqLj4iIqLRJTU3FwIEDcebMGejr62PVqlXw8/PTdlhKl9avR1p8PGyrVVN299a0RiNH4s7hw7gfFIToe/fU9vix3MQ+fIjUuDjIDA1hX7Omxl6HqDQoUsE9cOBAdcdBVGJVqlQJEokEEyZMwLhx44o8jiktMREv794FADh5eqoxQiIiotLF0NAQzs7OMDExQWBgIJo3b67tkJQSwsNx9U0X9w8nTIBUJiuW17WpWhVV2rTB/WPHcGndOrRbtEhjr5XVI8/RwwMyAwONvQ5RaVCk23BDhgzBpEmT1B0LUYnUpUsXHD9+HOPHj1dp0pCIa9cAIWBRoQJM7e3VGCEREVHpIpFIsGjRIvz+++86VWwDwPkVKyBPT0d5Ly+4tmhRrK/d6PPPAQB3jhxB3OPHGnsddicnKrgiFdxbt27Fw4cP1R0LUYmQnJyMCRMmIDIyUrmsphq6U7E7ORERUd7u37+PadOmITMzEwCgp6eHqhrsNl0UL+/cwa0DBwAAzSdOLPbZu+1r14Zry5YQCgUurV+vsdfhnDNEBVekgtvR0ZHT/1OZFBsbi549e2LHjh0YOnQohBBqOzZn+yQiIsrd1atX0alTJ2zcuBHLli3Tdjh5+nvpUkAIVG3bFo5162olhqy73CEHDiA+LEztx0+Ni0NMaCgAfmchKogiFdxt2rTB33//jYyMDHXHQ6SzwsPD0aVLF1y+fBmWlpaYPXu22i48KeTy113KwavFREREbztz5gy6d++O2NhYeHp65vt4Wm16dukSHp46BYlMhqZff621OJzr14dLkyZQZGYieMMGtR8//OpVAK9nRjcuV07txycqbYpUcH/77bdIS0vD8OHDkZCQoO6YiIpEIZfj6YULuH34MJ5euACFXK62Yz948AAdO3bE3bt34ejoiAMHDuCDDz5Q2/Gj791DelISDExNNTqrKBERUUly+PBh9OvXD8nJyWjWrBl2794Na2trbYeVgxACZxcvBgB49OyJcpUrazWerLvc/+3Zg8S3hsCpg3L8NofAERVIkR8L1q5dO2zevBlHjhzBxx9/DFdXVxgbG+fYViKRYMaMGSoHSpSf+0FBODVvHhIjIpTLzBwd0WraNFTx9VXp2NevX8dnn32GmJgYuLm5YefOnahQoYKqIWeTlbwc69UrttlMiYiIdNmWLVswZcoUCCHg5+eHlStXwkBHZ8QOPXYMEdeuQc/YGN5ffKHtcFChUSM4N2iA55cv43JgIFpMmaK2Y3P8NlHhFKjgbt26Ndq1a6ecmfzbb79VdqWNjo7Grl27cuwjkUgghGDBTRp3PygIh8eOBd4ZT50YGYnDY8fCb/nyIhfdQghMmzYNMTEx8PDwwLZt22Bra6uOsLPh+G0iIqL/FxkZiW+//RZCCPTv3x/z5s2DTEcvSMszMvD3kiUAgA8GD4apnZ2WI3r9PbzRqFE4MHw4ru/ahYYjRsBEDT0D5BkZiLh+HQC/sxAVVIEK7lOnTsHV1VX5+8yZMzlpGukEhVyOU/Pm5Si2AbxeJpHg1Lx5cPPxKdKdY4lEgvXr12P+/PmYO3cuzM3N1RB1TpyhnIiI6P85ODhg3bp1uHz5Mvz9/XX6e2fI/v2IffQIxtbWaKBD48srNWsG+9q18eK//3Bl0yZ8OG6cysd8efs2MlNTYWhpCWs3NzVESVT6FalL+bfffqvmMIiKJiw4OFs38hyEQGJEBMKCg+Hi7V3g44aGhsLd3R0A4OTkhBUrVqgaap6SXrxA/LNngEQCx3r1NPY6REREuiwjIwNhYWHKmzw+Pj7w8fHRblDvkZmaigurVgEAvEeNgqGZmZYj+n9Zd7kPf/UVrm3dig+GDIGRpaVKx1SO3/b0hERapKmgiMoc/qVQiZYUFaXW7QBg9erVaNmyJQ4ePFjUsAola7ZP22rVdCpRExERFZeUlBQMHToUHTt2xKNHj7QdToE9PngQyS9fwtLFBR69emk7nBzcW7eGTdWqSE9KwtWtW1U+3nOO3yYqNBbcVKIVdJxUQbYTQmDOnDkICAiAQqHA7du3VQ2vQNidnIiIyrL4+Hj06dMHx48fR2JiIh4/fqztkAokJSYGD/bvBwA0/fpryHRwQjeJVIpGo0YBAK5s3oz0xESVjsc5Z4gKjwU3lWjlGzaEmYNDvtvoGRmh3HvGGWVmZmL8+PH46aefALyepyBrkkBNe87kRUREZVRkZCS6dOmCixcvwsLCArt27ULLli21HVaBXFq7FvKUFNjVqoVqn3yi7XDyVLVtW5RzdUXaq1e4vnNnkY+TEB6OxIgISGQyOHp4qDFCotKtwAX3pk2bIJPJCv2jp1ekYeJEBSKVyeDSuHG+22SmpmJbp064HxSU6/rU1FQMGzYMu3btgkwmw7Jly/D5m+dXalpmWhpe3LoFgN2ziIiobHn06BE6d+6MkJAQ2NvbY//+/fDy8tJ2WAXy6ulT3HhTvDYdP16nxzNLZTJ4jRwJALgcGIiMlJQiHSerR55djRrQNzFRW3xEpV2BPx2EEEX+IdKUuMePcfd//wMAGFpYZFtn5uiI5hMnwqZqVaTExODwmDH43d8fqXFxym3S09PRt29fBAUFwcDAABs2bEDPnj2LLf7ImzehyMiAiZ0dLNT8bG8iIiJdFRoaik6dOuHx48eoVKkSfvvtN9SsWVPbYRXYueXLocjMhI2nJyo2aaLtcN6rup8fLMqXR3J0NG7++muRjqF8/jaHwBEVSoFvP7dr1w6TJ0/WZCxEhSKEwPEZMyBPS4NLkybovH49nv/7L5KiomBqZ4fyDRtCKpOhXv/+uLBqFYLXr8edw4fx9MIFfPzdd3D76CPo6+ujbt26uHnzJjZu3IgmxZw0lcnL01OnH3lCRESkTo6OjqhQoQLs7Oywfft22NvbazukAntx6xbuHD4MAKg+YICWoykYmb4+Gg4fjhPffot/N2yAR+/e0CvkmHMOgSMqmgIX3I6OjiVmTA2VDTd//RXPLl6EnrExPv7uO8j09HJ99JeegQE+HDcO7j4++GPKFMQ+eICDo0ahZufOaDltGmbMmIHBgwejYsWKxX4OTF5ERFQWmZqaYuvWrZDJZLB4p4earju7eDEAoNqnn8KiBD2LulbXrriwejUSIyNxa98+1O3du8D7piclIerNZLIcAkdUOLo74IQoH4mRkTizcCEAoOnYsbB0cXnvPo5166Lv/v2w9fPD3shI3Ni/H1s7dMCTv//WSrEthGD3LCIiKjP27NmDH3/8Ufl7uXLlSlyx/fjvv/Hk3DlI9fXReMwYbYdTKHoGBmg4bBgAIHj9esgzMgq8b+SNGxByOcydnGDu5KSpEIlKJRbcVOIIIXBi9mykJybCoW5dePbvX+B9r964gYDDh3FPTw/nFAokRkbiwPDhOD5jBtJUfFRGYcU9foyUmBjIDAxgV6tWsb42ERFRcVq/fj3GjBmD+fPn4+zZs9oOp0iEQoG/f/gBAFC3Tx9YlsC5V+r06AETGxvEh4Upu8UXRPjVqwDYI4+oKFhwU4lz73//w4MTJyDV10ebgABIZbIC7XfixAn07NkT8fHxaNSkCdadOQPPN2Ovbv76K7Z27Ign589rMvRssu5uO9SpU+hxVERERCWBEAILFizArFmzAAAjRoxA06ZNtRxV0dw5ehQvbt2CgakpGhXT00zUTd/YGA0GDwbw+rFmCrm8QPtlzVDu5OmpqdCISi0W3FSipMTG4lRAAADAa8QI2FarVqD9Dhw4gEGDBiE1NRWtW7fGzp07YevoiFbTpqH75s2wqFABCc+fY9/gwa/vniclafI0ALyVvNidnIiISiG5XI4pU6Zg+fLlAICpU6di1qxZkOrwI7TyIk9Px7llywAADYcPh4m1tXYDUkHd3r1haGmJ2EePcO+PP967vVAolHe4OQSOqPAK9ImnUCjwyy+/aDoWovf66/vvkRwdDesqVZTPlHyfwMBAfPnll8jMzESXLl0QGBgIk7eeH1mhUSP0++031P3sMwDA9R07sLVTJzy7eFEj55BFmbzYPYuIiEqZ9PR0jBo1Clu2bIFUKsXChQsxevToEvtEjhu7diH+2TOY2NmhfgmZmTwvBmZmynO4+NNPEApFvtvHPHiAtPh46Bkbw7Z69eIIkahUKXmXGKnMenTmDEJ++w2QSNAmIKBA3bBfvnyJBQsWQAiBIUOG4Mcff4S+vn6O7QxMTdF65kx0/eUXmDs7I/7ZM+wZMACn5s1DRkqK2s8lNT4e0ffuAWD3LCIiKn1Onz6Nw4cPQ19fH2vXrkW/fv20HVKRpSUm4sLq1QCAxl9+Cf23LtqXVJ79+sHA1BTR9+7hwcmT+W6b1SPPsW5dyHL5DkVE+WPBTSVCemIi/nwz/suzf/8CF6m2trbYvHkzpkyZgjlz5ry3G1vFpk3R7+BB1OnRAwBwdfNmbOvcWZls1CXr7rZVpUowsbFR67GJiIi0rU2bNpg1axa2bt2KTz/9VNvhqOTyL78gJTYW5VxdUad7d22HoxZGlpao17cvAODC6tUQQuS5rfKJKuyRR1QkLLipRDi3bBkSnj+HRfnyaDp2bL7bZmRk4PabZ0UCQKNGjTBmzJgCd2MzNDPDx3PmoPP69TBzcEDc48fY3bcvzixciMzUVJXOI0s4n79NRESlzPPnzxEdHa38feTIkWjevLkWI1JdUlQU/g0MBAB8OH48pHp6Wo5IfeoPGgQ9Y2O8+O8/PM5n5nh+ZyFSDQtu0nnPL1/G1W3bAAA+330HA1PTPLdNSkrCgAED0LlzZ9y6dUul13Vt3hz9Dh1CrS5dACHw7y+/YFvXrgi/dk2l4wK8WkxERKXLvXv30KFDB/Tr1w+JxfyYTU36Z9UqZKakwLFePbi3aaPtcNTKxNoadXv1ApD3Xe7kmBjEPnoEgEPgiIqKBTfptMz0dByfMQMQArW6dEGlDz/Mc9vY2Fj06tULp0+fRkZGRrar7EVlZGEB3/nz0XH1apjY2SH2wQPs7tMHfy9Zgsz09CIdU5GZiYjr1wFwtk8iIir5rly5gs6dOyM8PBxJSUmlpuCOffgQN3/9FQDQzN+/xE74lp8GgwdDZmCA8CtXcp0sNmsInHWVKjCytCzm6IhKBxbcpNMurVmDmNBQmNjaosXkyXluFx4ejs6dO+Py5cuwsrLC7t271dqNza11aww4dAjV/fwgFApcWrcOO7p1w4v//iv0sV7euYOM5GQYWljA2t1dbTESEREVt7/++gs9evRAbGwsPD09ceDAATg6Omo7LLU4t2wZhFyOyq1aoYKXl7bD0QgzBwfUfjMu/eKaNTnWs0cekepYcJPOirpzB5fWrQMAfDR9OoysrHLdLjQ0FB07dsS9e/fg6OiIAwcO4IMPPlB7PEZWVvhk8WL4rVgBY2trRN+7hx09e+L8ihWQF+Ju9/OssVCenpCUwGeREhERAcChQ4fQv39/JCcno3nz5ti9ezesS/Dzqd8Wfu0a7v3xByRSKT4cP17b4WhUw6FDIdXTw9Pz55XfUbJkTRrL8dtERcdv+6STFJmZOP7NN1BkZsK9TRtUads21+1CQ0PRqVMnhIWFwc3NDQcPHkS1atU0GlsVX1/0P3wYVdu2hZDLcWH1auzs1QtRd+4UaH8mLyIiKun27duHzz//HBkZGejQoQO2bNkCMzMzbYelFkIInF20CABQs3Nn2Gr4e4W2WZQvj5qdOgF43bMwizw9HZE3bgDgHW4iVbDgJp10ZfNmRN68CQNzc3w0fXqe46ZcXFxQq1Yt1K1bF7/99hsqVKhQLPGZWFvj0+XL8cmSJTCytERUSAh2dO+Oi2vWQJGZme++WeOhmLyIiKikatCgAWxtbdG/f3+sXr0aBgYG2g5JbR799RfCgoMhMzBAk9GjtR1OsWg4fDgkUikenj6tHC73IiQE8vR0GJcrBytXV+0GSFSCseAmnRP35AnOr1gBAGgxeTLMHBzy3NbAwAC//PILfv31V9ho4XnW1du3R//Dh+Hm4wNFRgbOLVuGnb16IfrevVy3T4iIQMLz55DIZHDw8CjmaImIiNTD1dUVQUFB+P777yGTybQdjtoo5HKcXbwYAODZvz/MnZy0HFHxKOfqimrt2wP4/7Hc4W/1yCuNE8YRFRcW3KRThBA4PmMGMlNT4dK4MWp365Zjm+3bt2POnDnKx1eYmZnB3Ny8uENVMrWzQ4eVK9F24UIYWljgxX//YXvXrri0fj0UcrlyO4Vcrpzt1LJCBegZGWkrZCIiokLJyMjAuHHjcPz4ceUyBweHUleI3T54ENH37sHQwgJeI0ZoO5xi1ejzzwEA948dw3/79uHO0aMAAMd69bQZFlGJx4KbdMp/e/bg2YUL0DMygs9332VL5EIIrFq1Cv7+/vjpp59w4sQJLUaanUQiQc2OHdH/0CG4tmwJeUYG/v7hB+z+7DPEPHiA+0FB+MXHBxdWrQIAxD1+jF98fHA/KEjLkRMREeUvJSUFQ4cOxa5du/DFF18gLi5O2yFpRGZamrKHndfIkWXuMVg2Vaooi+tj06Ypx29f2bSJ31eIVMCCm3RGYmQk/lq4EADQdOxYWFWsqFwnhMCcOXMwd+5cAMBXX32F1q1bayXO/Jg5OKDTmjVoM3cuDMzMEHHtGrZ27IjDY8YgMSIi27aJkZE4PHYskxgREems+Ph49O7dG8ePH4ehoSFWr14NqzyeGlLSXdu6FQnh4TBzdIRn377aDqfY3Q8KQsS1azmWp8TG8vsKkQpYcJNOEELg5Jw5SE9IgIOHBzwHDFCuy8zMxLhx47DmzZiimTNnYtq0aTrbjU0ikaB2t27od/AgXJo0yXsStTdd4k/Nm5et6zkREZEuiIyMRJcuXXDp0iVYWFhg165d+Pjjj7UdlkakvnqFi28eRdpkzJgyN+xLIZfj1Lx5ua/k9xUilbDgJp0QGhSE0OPHIdXTQ5uAAEjfTMCSmpqKYcOGYffu3ZDJZFi2bBk+fzPGSNdZODsrx0PlSQgkRkQgLDi4eIIiIiIqgEePHqFTp04ICQmBvb099u/fj0aNGmk7LI0J/vlnpL16BZuqVZWPyCpLwoKDc/TEy4bfV4iKTE/bARClJyTg/Juu4l4jRsC2enXlukuXLuHYsWMwNDTE2rVr4evrq60wiyQpKkqt2xERERWHrVu34smTJ3B1dcWOHTtQqVIlbYekMQnh4biyeTMA4MPx45UX/csSfl8h0hwW3KR1dwIDkRIdDWt3d3i9c0e4efPmWLx4MSpXrozGjRtrKcKiM7WzU+t2RERExWHq1KmQyWQYOnQo7O3ttR2ORv2zciXkaWko37AhKrdqpe1wtILfV4g0h13KSaue/P03wk6cACQSfBwQAD0DAzx58gTPnz9XbtOnT58SWWwDQPmGDWHm6AjkNd5cIoGZoyPKN2xYvIERERG9499//0VGRgYAQCaTYerUqaW+2I6+dw+39u8HADTz99fZ+WE0jd9XiDRHZwvutLQ0TJ48Gc7OzjA2Noa3tzeOHTv23v327duHXr16wc3NDSYmJqhevTomTJhQah9hUZKlJyXhxLffAgDq9e0L5/r1cfv2bXTq1Al9+vRBbGysdgNUA6lMhlbTpr3+5d0k9ub3VtOmlcnua0Ske5h7y65ff/0VnTt3hr+/PxQKhbbDKTZ/L1kCoVCgSps2cPL01HY4WsPvK0Sao7MF96BBg7BkyRL07dsXy5cvh0wmQ/v27XH27Nl89xsxYgRCQkLQr18/rFixAu3atcPKlSvRpEkTpKSkFFP0VBDnli9HwvPnMLKzQ+MxYxAcHIzOnTsjMjISMpkM6enp2g5RLar4+sJv+XKYOThkW27m4AC/5ctRpYSNSyei0ou5t2xat24dxo4dC/mbGahLe8GtkMvx9MIFnF+xAg9OngSkUjQdN07bYWkdv68QaYZOjuG+ePEidu7ciUWLFsHf3x8AMGDAANSpUweTJk3CuXPn8tx3z549aPXO+JsPPvgAAwcOxLZt2zBs2DBNhk4F9PzKFVzdsgUAUPuLL3Dmn3/w+eefIzU1FQ0bNsSWLVtgaWmp5SjVp4qvL9x8fBAWHIykqCiY2tmhfMOGvFJMRDqDubfsEULg+++/x6pVqwAAI0eOxIwZMyCV6uz9GJXdDwrCqXnzss3IrW9oiJj792Ht5qbFyHQDv68QqZ9OfqLu2bMHMpkMI0aMUC4zMjLC0KFDcf78eTx9+jTPfd9N+ADQpUsXAEBISIjaY6XCy0xPx/EZMwAhUKNTJ1x9+RLDhg1DamoqWrdujV27dpWqYjuLVCaDi7c3avj5wcXbm8mLiHQKc2/ZIpfLsXjxYmWxPW3aNMycObPUF9uHx47N8firjJQUHB47FveDgrQUmW7h9xUi9dLJT9UrV66gWrVqsLCwyLY86/mPV69eLdTxIt58sNra2qolPlLNpTVrEHP/PkxsbBBbuzbmzJmDzMxMdO3aFYGBgTA2NtZ2iEREZQ5zb9kyceJEHDp0CFKpFIsWLcJXX31VaicMU8jlSHzxAidmzwaEyHO7U/PmQfGmWz0RkbroZJfy8PBwODk55VieteztGawLYsGCBZDJZOjevXu+26WlpSEtLU35e3x8PIDXV4HluXwAy+VyCCF0eqxTbnFr08u7d3Fp3ToAQItvvoGphwecnJzw6aefYvbs2ZBKpToXs66Ty+VQKBRsNxWwDdWjJLSjLn9eA6/jk0gk+bahTEN3m5h71UuX/w4AoGPHjvjtt9+wbNkydOjQoUDxKuRyPP/3XyRHRcHEzg7OH3ygtbufQgikJyYi+eVLJEdHv/7vy5dIevPfbD8xMRDvOz8hkBgRgacXL6LCm4tM71MSPvN0HdtQdSWlDXX5M7sguRcoev7VyYI7JSUFhoaGOZYbGRkp1xfU9u3bsWHDBkyaNAlVq1bNd9v58+dj9uzZOZaHhobCzMwsx3KFQoGUlBTExMTo7FXhe/fuaTsEJSGX458pUyDPyICDtzdE5cpISEjA999/j4oVKyI0NFTbIZZICoUCMTExuH//fqnuCqhJbEP1KAntGB0dre0Q8iWEgEQiybcNa9SooZHXZu5VL13Kv1my3l/A6wspP/30EypVqlSgWCPOn0fIzz8j7a2/IUMbG9QcNgyOTZqoLUZ5ejrS4+KQFheHtNhYpMXFIf3Nf9/9XaGByVVDb9xASrlyBdq2JHzm6Tq2oepKShvqcv4tSO4Fip5/dbLgNjY2zna1O0tqaqpyfUGcOXMGQ4cORdu2bTF37tz3bj916lSMHz9e+Xt8fDxcXFzg7u6eo4sd8PqKUnR0NKytrXX2Df6+LzrF6crGjYi5excHYmMxomVLVKtWTXklqUqVKhq7a1PayeVy3L9/n22oArahepSEdhT5dCfVBVlX2bXRhsy96qVL+RcAwsLCMHr0aCxatAju7u6Fyr/3jx3D1YULc3THTouJwdWFC/HJ0qWo0qZNnvsr5HKkxsbmevdZuezNXeq0Nz0cCsrAzAwmtrbKH9O3/t/Exkb5/zH37+O34cPfezx3Dw9UKOC/XUn4zNN1bEPVlZQ21OX8q+ncq5MFt5OTE8LCwnIsDw8PBwA4Ozu/9xjXrl1Dx44dUadOHezZswd6eu8/VUNDw1yv7stksjwbXyKRQCqV6mzS15U/vFdPn+L0smXYFh6OZ3p6+HbePHzSqRPKlSsHqVSabxvT+7ENVcc2VA9db0dd/ax+m0Qi0UobMveqly79Ddy9exe9e/dGREQEJk2ahP379wMo2N+rQi7Hme+/z33s85tlp777DiIjAymxsUiKispWSCe9fImU6GiIQnQnlenrw8TOLlsBbWprCxM7u///3c4OJjY20C/ghSBze3uYOToiMTIy93ORSGDm4ACXRo0K1U1e1z/zSgK2oepKQhvq8uc1oNncq5MFt6enJ06ePIn4+PhsV7cvXLigXJ+f0NBQtGvXDvb29jh69GiuXdKo+AghcGDSJGx4+BARCgWs7e2xYcMG2Nra6vx4EyKisoK5t3S6fPky+vXrh7i4OFStWhWrV68uVFf8sODgHLN6vyslJgb/mzgx/wNJJMo7ztmK5nd/t7GBoYWF2ocLSGUytJo2DYfHjgUkkuxF95vXajVtGmfkJiK108mCu3v37li8eDHWrVunfBZoWloaAgMD4e3tDRcXFwDAkydPkJycnK0/fUREBHx9fSGVSvHHH3/Azs5OK+dA/+/0hg2Y89tviMrIgJOrK7bv2oUGDRpoOywiInoLc2/p89dff2HIkCFITk5G/fr1sXXrVpQr4PjkLElRUQXarpybG+xq1MizoDYuVw7SAvR40KQqvr7wW748x3O4zRwc0GraNFTx9dVidERUWulkwe3t7Y0ePXpg6tSpePHiBapUqYJNmzbh0aNH2LBhg3K7AQMG4PTp09nGBLRr1w4PHjzApEmTcPbsWZw9e1a5zsHBAW3yGWNE6nfj4kUMHDsWcenpcCpfHgePHEG1atW0HRYREb2Dubd0OXjwIEaPHo2MjAy0aNECGzZsgKmpqcZer/WsWXDx9tbY8dWliq8v3Hx8EBYcjKSoKJja2aF8w4a8s01EGqOTBTcAbN68GTNmzMCWLVsQGxuLunXr4vDhw2jRokW++127dg0AsHDhwhzrWrZsyaRfzJZPmIC4tDQ4Wlkh6PRpVHR11XZIRESUB+be0kGhUCAwMBAZGRno0KEDfvzxRxgYGBTqGEII3Ni1C38tWJD/hm/GPpdv2FCFiIuXVCYrERcHiKh00NmC28jICIsWLcKiRYvy3ObUqVM5lunyDHhlzb0//kDtFy8QZ2eHufv2sdgmItJxzL2lg1QqxcaNG7Fx40Z89dVXhZ4EKCE8HMe++QZPzp0DAFi7uyMmNJRjn4mIikC3p4ujEuns2bN4FRmJk3PmQCKRYPyUKaiuxmd0EhERUXYKhQInT55U/m5paYmxY8cWqtgWQuC/vXuxpUMHPDl3DnpGRmg5bRr6HzoEvxUrYObgkG17MwcH+C1fzrHPRET50Nk73FQybdu2DZMnT0Yta2u0TUuDrbs7Go0ape2wiIiISq2MjAyMHz8ee/fuRUBAAIYMGVLoYyRGRuL4zJl4dPo0AMDJ0xO+8+ejXOXKADj2mYioqFhwk1oIIbBq1SrMmzcP8vR0ZDx7Bom9PdoEBECvkOPGiIiIqGBSUlIwYsQI/Pnnn9DT04OlpWWh9hdCIOTgQZwKCEBafDxkBgZoOnYs6g8alKOY5thnIqLCY8FNKlMoFJgzZw7Wrl0LCIFmlpZopqcHz7594czHfxEREWnEq1evMGDAAFy6dAlGRkZYv349fHx8Crx/WlwcjowZg4cnTgAAHDw84Dt/PmyqVNFUyEREZQ4LblJJZmYm/P39sXv3bgBAb29vVLx9G+bOzvhw3DgtR0dERFQ6RUZG4rPPPkNISAgsLCywZcsWeHl5FXj/u7//jrOzZyMjIQFSfX00/vJLNBw2TOvPyiYiKm34qUoqySq2ZTIZpo8ejZTAQACAz+zZMDAz03J0REREpU9ycjI6d+6Mx48fw97eHjt27EDNmjULtm9MDE5+9x3u/e9/AAC7mjXR9vvvYVu9uiZDJiIqszhLOamkf//+KFeuHNatWQOTM2cAIVCzUye4Nm+u7dCIiIhKJRMTEwwYMACurq747bffClxs3w8KwhY/P9z73/8g1dNDld690WPHDhbbREQaxDvcVGhCCEjePHvzgw8+wIULF3Bz40bcuXcPxtbWaDFlipYjJCIiKn3ezr+jRo1C//79YVaA3mSpcXE4GRCAO4cPAwBsq1WDz9y5eKWvD5m+vkZjJiIq63iHmwrlyZMnaNeuHW7cuKFclvr8OS6uXQsAaDV9OozLldNWeERERKXSsWPH0KlTJ8THxyuXFaTYfnDyJDZ36IA7hw9DIpOh0eefo/eePbCvVUuT4RIR0RssuKnAQkJC0KlTJ9y4cQNTp06FEAIKuRzHZ8yAIiMDbq1bo9onn2g7TCIiolJl9+7dGDJkCIKDg7Fu3boC7ZMaH48/pkzBwVGjkBwVBWt3d/TauRNNv/6aj+skIipG7FJOBRIcHIx+/fohPj4eNWvWxIYNGyCRSHBl61ZEXLsGAzMztJ41S9nVjYiIiFS3du1azJ49GwDQs2dPfP311+/d59GZMzg+fToSIyMBiQQfDBmCJmPGQM/QUMPREhHRu1hw03v9+eefGD58OFJTU+Hl5YXNmzfD0tISr549w7llywAAzSdOhJmDg3YDJSIiKiWEEPj+++/x448/AgA+//xzTJ8+HVJp3p0T0xITcWbBAtz89VcAgFWlSvCdPx/ODRoUS8xERJQTC27K1759+/D1118jMzMTPj4+WLduHYyNjSGEwJ8zZyIzJQXlvbxQp0cPbYdKRERUKsjlckyZMgXbtm0DAHzzzTf44osv8u1F9uT8eRz75hskPH8OSCSoP2AAmn79NfSNjYsrbCIiygULbsqTEAL79u1DZmYmunXrhiVLlkD/zWymt/btw5Nz5yAzNMTHc+ZAks8VdyIiIiq4ly9f4vjx45BKpViwYAH69u2b57bpSUk4+8MPuL59OwDA0sUFbebNQwUvr+IKl4iI8sGCm/IkkUiwbt06bN++HUOGDFF2Y0uKisJfCxYAAJqMHo1yrq5ajJKIiKh0cXBwwM6dO/HgwQN8ks9kpM8uXcKxadPw6ulTAEDdzz5DswkTYGBqWlyhEhHRe7DgpmzkcjmOHDmCDh06QCKRwMTEBMOGDcu2zck5c5AWHw/7WrXQYNAg7QRKRERUikRHR+PWrVto3rw5AKB69eqoXr16rttmpKTg76VLcXXLFkAImDs7o83cuajYpElxhkxERAXAgpuU0tPTMXr0aBw6dAj37t3DhAkTcmxzPygI94OCIJHJ8PHcuZDq8S1ERESkimfPnqF379549uwZtm/fjqZNm+a57fPLlxE0dSriHj8GANTp0QPNJ0+GYQGeyU1ERMWP1RIBAJKSkjB06FD89ddf0NfXR9WqVXNsk/rqFU7MmQMAaDhsGOxr1izuMImIiEqVu3fvonfv3oiIiICzszMc8njiR2ZaGs6vWIHLgYEQCgXMHBzwcUAAXN/cESciIt3EgpsQExOD/v3748qVKzAxMcEvv/yCFi1a5NjuzMKFSI6KQrnKleH9xRdaiJSIiKj0uHz5Mvr164e4uDhUrVoVO3bsgLOzc47tIm7cQNCUKYgJDQUA1OrSBS2mToWRhUVxh0xERIXEgruMe/78OXr37o379+/DysoKW7duRYNcntf55Px5/Ld3LwDg44AA6BkaFneoREREpcbp06cxdOhQJCcno379+ti8aROSQ0Nx+/JlmNrZoXzDhlDI5biwahWCf/4ZQi6HiZ0dPp49G26tW2s7fCIiKiAW3GVYWloaunbtiidPnsDJyQk7d+7M1pVcIZcjLDgY8WFh+HvJEgBAvc8+Q/kPPtBWyERERCXetWvXMGDAAGRkZKBFixaY9tln2N+9OxIjIpTbmNjYQGZggITwcABAdT8/fDR9OoysrLQUNRERFQUL7jLM0NAQY8eOxU8//YQdO3agfPnyynX3g4Jwat68bMlfIpXC0dNTC5ESERGVHh4eHmjfvj2EEBjToQOC/P0BIbJtkxwdDQAwMDOD77x5qOLrq41QiYhIRSy4y6DMzEzovZldvE+fPujatSsM3+oifj8oCIfHjs2R/IVCgT8mT4a+kRETPxERUSEIISCXy6GnpwepVIoVK1YAQmCTr2+OfPs2fRMTuPn4FGOkRESkTlJtB0DF68iRI2jbti2i31w5B5Ct2FbI5Tg1b16+yf/UvHlQyOUajZOIiKi0UCgUmDlzJsaMGQOFQgEA0NfXR8SVK9l6kuUm6cULhAUHF0eYRESkASy4y5Bt27Zh5MiRCAkJwfr163PdJiw4OP/kLwQSIyKY/ImIiAogIyMDY8aMwYYNG3DgwAH8888/ynVJUVEFOkZBtyMiIt3DLuVlgBACK1euxPz58wEAffv2xcSJE3PdlsmfiIhIPVJSUjBixAj8+eef0NPTw7Jly9C0aVPlehMbmwIdx9TOTlMhEhGRhrHgLuUUCgW+++47rFu3DgAwevRoTJkyBRKJJMe2QgiEXbpUoOMy+RMREeXt1atX6N+/P4KDg2FkZIT169fD562x2OlJSbiyeXP+B5FIYObggPING2o4WiIi0hQW3KVYRkYGxo0bh19//RUAMGvWLIwcOTLXbVNfvULQ1Kl4cOJE/gdl8iciIsrXy5cvMWrUKNy+fRsWFhbYsmULvLy8lOsTIyPx26hRiLp1C1I9PSgyMwGJJPv8KW8ujLeaNg1Smay4T4GIiNSEY7hLsaSkJFy6dAkymQzLly/Ps9iOuHED27t2xYMTJyDT10ednj1fJ/p374Iz+RMREb3Xs2fPEBoaCgcHB+zfvz9bsf0iJAQ7e/ZE1K1bMLa2Ro+tW+G3YgXMHByyHcPMwQF+y5fzqSBERCUc73CXYlZWVti5cyfu3r2Ljz/+OMd6IQSub9+Ov77/HvKMDFi6uODTZctgX7s2XJs1y/EcbjMHB7SaNo3Jn4iIKB+enp745ZdfUKVKFVSqVEm5/OGpUzg6fjwykpNh7e6OTmvXwrJCBQCAm48PwoKDkRQVBVM7O5Rv2JAXt4mISgEW3KVMdHQ0bt26hebNmwMAKlasiIoVK+bYLi0xEcdnzMC9338HALi3aYM2c+fCyMICAFDF15fJn4iIqICuXbsGc3NzuLm5AUC28doAcHXLFpyePx9CoYBLkyb4dPlyZc4FAKlMBhdv72KNmYiINI8FdykSFhaGCRMmICIiAnPnzsWHH36Y63ZRd+7gyJgxiHv8GFI9PTTz90f9gQNzTKTG5E9ERPR+Z8+exaxZs2BhYYE1a9bA4a3u4Qq5HH/Nn4+rW7cCAGp3747Ws2ZBpq+vrXCJiKgYseAuJUJDQzFhwgRER0fD2dk5Wxe2LEII3Nq3Dye++w7ytDSYOTqi/dKlcK5fXwsRExERlXxHjx7FwoULIZfLUb16dVhZWSnXpScl4fcJE/Dw1CkAwIcTJqDhsGG5PimEiIhKJxbcpcCNGzcwadIkJCYmwt3dHYsXL4atrW22bTKSk3Hiu+8QcuAAAMC1RQu0XbAAxuXKaSFiIiKikm/nzp1YtWoVAKB9+/aYOHEi9PRef7VKiIjAwVGjEBUSApmhIdotWICq7dppM1wiItICFtwl3Pnz5zFjxgykpaXBw8MDCxYsgLm5ebZtYkJDceTrrxF97x4kUimajB0Lr+HDIZFyknoiIqLCEkJg7dq12LZtGwCgT58+GDVqlPLO9avQUPz1/fdIevECJjY26LB6NZzq1dNmyEREpCUsuEuw+/fvY9q0acjMzETTpk0xe/ZsGBkZZdvm9qFD+HPWLGQkJ8PEzg6fLF7McdlEREQq2LVrl7LYHjVqFD777DPlusiLF3Ft8WLI09JgXaUKOq1Zo5yJnIiIyh4W3CWYu7s7/Pz8kJycjClTpkD/rQlY5OnpCNmwAU//9z8AQAVvb3yyeDFM7ey0FS4REVGp4Ofnh+PHj6Nz587w8/MD8Pqu9+PDhxGyYQMgBCo2bYpPly+H4Tu9zoiIqGxhwV3CCCGQmZkJfX19SCQSfP3115BIJJC+1T08KTwcVxcuRPyDB4BEAu9Ro+D95Zd8pBcREVERpaenw8DAAABgZmaGNWvWKMdrK+RyhPz8M54cPQoAcGnbFp0WL+ZM5ERExIK7JJHL5Vi2bBkiIyMxd+5c6OvrQ/ZOER1x/jxu/PgjMpOSoG9ujk+XLIHrm2dyExERUeHFxsZi4sSJ+Pjjj9G7d28AUBbbmcnJuLp4MaL+/ReQSFB94EBU7tyZxTYREQFgwV1ipKenIyAgACdPnoREIsG1a9fQsGFD5XpFRgbubN6MRwcPAgCsatSAp78/XJs101bIREREJV5ERATGjx+Pp0+f4sWLF/Dz84OZmRkAICUqCv8GBCDh0SNIDQxQb/x4ODZpouWIiYhIl7DgLgGSk5PxzTffIDg4GPr6+pg+fXq2YjslKgpXFy1C3J07AIDKnTujWv/+kOrxn5eIiKioHj58iAkTJiAqKgoODg5YunSpsth+df8+/g0IQFpsLAzLlUODb76BVdWqWo6YiIh0DSsyHRcXF4dJkyYhJCQExsbGmDt3Lry8vJTro/79F9eWLkVGQgL0TE1Rd+xYOHAWciIiIpXcvHkTkydPRnx8PFxdXfHDDz/A3t4eABB54QKu/fAD5GlpMKtUCQ1nzIAxJyUlIqJcsODWYZGRkZgwYQIeP34MS0tLLFy4ELVq1QLweoKW+9u3I3TPHgCAhbs76k+eDBMHB22GTEREVOJduHAB06dPR2pqKmrVqoWFCxfC0tISQgg8OngQtwMDASFgW78+PCdNgr6JibZDJiIiHcWCW4fFxcUhKioKdnZ2WLJkCVxdXQEAqTExuPbDD4i5eRMAUPGTT1Bj6FBO0EJERKQGz549Q2pqKry8vBAQEAATExMo5HLcWrdO+bhNl3btUGvECD4BhIiI8sWCW4dVr14dCxYsgJOTExze3LmOvn4dV3/4AelxcZAZGaHOl1/CuUULLUdKRERUenTr1g3lypVD8+bNoa+vj4zkZFxduBAvr1wBJBLUGDwYrh07QiKRaDtUIiLScSy4dcy///4LY2NjZddxT09PAIBQKBC6Zw/u7dgBKBQwr1QJnpMmwaxCBS1GS0REVPIJIXDgwAF8/PHHMDc3BwC0bt0awJuZyOfMQcLjx5AZGqLe+PFwaNxYm+ESEVEJwoJbh5w8eRJz5syBiYkJ1qxZgwpvium0V69wfenS11fWAVT4+GPUGjECMkNDbYZLRERU4ikUCqxYsQJ79+7FsWPHsGLFCuUztuPu3cO/AQFIj4uDYbly+GD6dFhWqaLliImIqCRhwa0jDh48iMWLF0MIgfr16ytnQo0NCcGVRYuQFh0NqYEBan/+OSr4+Gg5WiIiopIvIyMD8+fPx7FjxwAAPj4+ymI74vx5XFuyBIr0dJi7uuKD6dM5EzkRERUaC24tE0Jg69atWLduHQCgY8eOGD9+PKRSKR4eOIA7mzZBKBQwLV8e9SdNgvmbidOIiIio6FJTUzFjxgz8888/0NPTw9SpU+Hr6wshhDL/QgjYffABPP39oceZyImIqAhYcGuRQqHAqlWrsHv3bgBA//79MXz4cGQmJeHqihV4ceECAMCpeXPU+eILJnsiIiI1iI+Px+TJk3Hz5k0YGhpizpw5aNKkCRSZma9nIv/jDwBAxfbtUXPYMM5ETkRERcaCW4v27dunLLa/+uor9OrVC6/u3cOVRYuQEhkJiZ4eag4bhort2nEmVCIiIjUJCAjAzZs3YWZmhoULF8LDwwMZSUm4snAhoq9eBSQS1Bw6FJX8/Jh/iYhIJSy4tahDhw44c+YMPvnkE7Rt2xaPjx5FyIYNEJmZMHZwQP1Jkzg5CxERkZp9+eWXePHiBWbMmAF3d3ckR0bi34AAJD558nom8gkT4ODtre0wiYioFGDBXcxSUlJgZGQEiUQCQ0NDLF26FIrUVFz74QeEnzkDAHDw9obHmDHQNzPTcrRERESlQ0pKCoyNjQEAlSpVwi+//AKpVIq4O3fw79y5SH/1CobW1q9nInd313K0RERUWrDgLkbR0dHw9/dH06ZNMXz4cABA0pMnuLJgAZKeP4dEJkP1gQPh2rEju7ARUYn08uVLCCEglUq1HQqR0pUrVzBz5kzMnDkTXl5eAACpVIrwv//G9WXLXs9EXrny65nIbW3V8pp37txRy3E0RaFQ8LsGEVExYMFdTMLCwjBhwgSEhYUhNjYWPXr0QGJwMP5buxaK9HQY2djAc9IklKtRQ9uhEhERlRpnz57FzJkzkZGRgT179sDLywtCCDzYtw93N28GANg1bPh6JvI3d8CJiIjUhQV3MQgNDcWECRMQHR0NZ2dnLJo/H082bULYiRMAANsGDVBv3DgYWFhoOVIiIqLS4+jRo1i4cCHkcjmaNWuG2bNnQ5GZif9++gnPjh8HAFTy80PNIUMg4UzkRESkASy4Nez69euYPHkyEhMT4e7ujtkTJuDxsmVIfPwYkEpR7bPP4NatGyQa6n7JLm1ERFQW7dixA6tXrwYAtG/fHhMnToRITUXwggWIvn4dkEpRc+hQuPr5aTlSIiLVcDiXbmPBrUHnzp3DzJkzkZaWhrp16+Lrjh1xJyAA8tRUGFhZwXPCBNjUravtMImIiEoNIQTWrl2Lbdu2AQD69OmDUaNGIeXFCwR/9x2Snj2DzMgInhMnwr5hQy1HS0REpR0Lbg2Kj49HWloamnh7o7erK+6/udJuXacOPP39YViunJYjJCIiKl2EEHj58iUAYNSoUfjss88Qe+cOLmfNRG5jg4bTp8PCzU3LkRIRUVnAgluNhFyOmFu3kBYbC8Ny5dC2TRsYCwH88Qcijx0DALj37ImqvXtzrBgREZEGSKVSTJ48GR9//DEaN26M8LNncX35cijS02Hh5oYPpk+HkY2NtsMkIqIyQmcL7rS0NMycORNbtmxBbGws6tati4CAALRp0+a9+4aFhWHcuHEICgqCQqHARx99hKVLl8JNg1ezI86fx61163DywQN4mJnBXE8P+ubmkKenQ5GWBn1zc9QbPx52DRpoLIaSqiSMO6levbq2QyAqEXMyUMlX0vIvACTGx2PrTz/hU29vmNjYwLpWLXh7eyP0119xd+tWAIC9lxfqTZjAmcjfouv5l7mXdIUu51/mXt2nswX3oEGDsGfPHnz99deoWrUqNm7ciPbt2+PkyZNo1qxZnvslJibio48+wqtXrzBt2jTo6+tj6dKlaNmyJa5evQobDVzVjjx/HpcXLMCRly8R/OoVriYkYGSFCkBCAgDApHx5NJo9G8Z2dmp/bSIiInUqSfkXAG4HBWHqzJkIS0hAyMGDaGdrC0MbG5g6OyPmxg0AgGuHDqgxeDB7lxERUbHTyYL74sWL2LlzJxYtWgR/f38AwIABA1CnTh1MmjQJ586dy3Pf1atX4969e7h48SK8vLwAAJ988gnq1KmDH374AfPmzVNrrEKhwI316/FrZCRuJSZCIpHAy8ICsrdm3lakpsLI2lqtr0tERKRuJSn/AsD1I0cwefp0RKenw1QmQ11zcwBAWnQ00qKjAYkEtUaMQKX27dX+2kRERAWhkwX3nj17IJPJMGLECOUyIyMjDB06FNOmTcPTp0/h4uKS575eXl7KZA8ANWrUgI+PD3bv3q32hB914wZ++e8/PEhOhkwiQVcHB9QxM8u2TWp0NGJu3YKNh4daX5uKj653JYqOjtbpboG6jm1I9FpJyr8PQ0MxYeZMvEpPh6W+PgY4OcHWwCDbNvrm5qjYtq1aX5eKjy7nXoC5Qx3YhlQW6OQ7+8qVK6hWrRosLCyyLW/UqBEA4OrVq7nup1AocP36dTTM5TEfjRo1QmhoKBLedPNWh5iYGMxduRIPkpNhIJWir5NTjmI7S1psrNpel4iISBNKSv69c+cOvvz8c7xKTYWdgQGGlS+fo9gGgIz4eMTcuqW21yUiIiosnbzDHR4eDicnpxzLs5Y9f/481/1iYmKQlpb23n3zmoQjLS0NaWlpyt/j4+MBAHK5HHK5PMf2/v7+eBQeDmOZDP2dnFDeyCjPczKwsuKkBrlQKBQQQrBtVMA2VB3bUD3YjqpTKBSQSCS55pwsMg2OQ9ZG/i1s7o2Pj8fcuXMRn5iICkZG6OfkBON82iQ1JobvyVzw71V1bEPVsQ1VxzZUXUFyL1D0/KuTBXdKSgoMDQ1zLDd6U9CmpKTkuR+AIu0LAPPnz8fs2bNzLA8NDYVZLneuBw0ahPv378M3JQWWycl5HtfI1hbWtWpB8ta4bnpNIpFk+6HCYxuqjm2oHmxH1UkkEsTExOD+/ft5dq+sUaOGxl5fG/m3sLlXoVBg9OjR+GP/frSOiIDBe7qhGpYrx/djLvj3qjq2oerYhqpjG6quILkXKHr+1cmC29jYONvV7iypqanK9XntB6BI+wLA1KlTMX78eOXv8fHxcHFxgbu7e47udQDg5uaGdevWQfr4Mf6YMOH1QiH+f4M3b/qPpk9HFQ1+QSrJ5HI57t+/jypVqmj0rk1pxjZUHdtQPdiOqtN2G2oj/xY292bdgfh85Ehs++QTJL54kT33ZpFIYObggEadOkHK92MO2n6vlQZsQ9WxDVXHNlSdpttQJwtuJycnhIWF5VgeHh4OAHB2ds51P2traxgaGiq3K8y+wOsr87ldnZfJZHk2vkwmQ9W2baGvp4dT8+YhMSJCuc7MwQGtpk1DFV/fPF+TAKlUmm8b0/uxDVXHNlQPtqPqtNmG2si/Rcm9UqkUBoaGaPXNNzg8duzrC9y5XPBuNW0a9HMZ202v8e9VdWxD1bENVcc2VJ0m21AnC25PT0+cPHkS8fHx2a5uX7hwQbk+N1KpFB4eHggODs6x7sKFC3Bzc4P5m0eGqFsVX1+4+fggLDgYSVFRMLWzQ/mGDXlVnYiISoySln+r+PrCb/lyXvAmIiKdpZOzlHfv3h1yuRzr1q1TLktLS0NgYCC8vb2VjyR58uQJbt++nWPfS5cuZUv6d+7cwYkTJ9CjRw+Nxi2VyeDi7Y0afn5w8fZmsU1ERCVKScy/VXx9MeTPP9Ft0ya0W7wY3TZtwpA//2SxTUREOkEn73B7e3ujR48emDp1Kl68eIEqVapg06ZNePToETZs2KDcbsCAATh9+jTEW93IvvjiC6xfvx6ffvop/P39oa+vjyVLlsDBwQETssZZExERUQ4lNf9mXfAmIiLSNTpZcAPA5s2bMWPGDGzZsgWxsbGoW7cuDh8+jBYtWuS7n7m5OU6dOoVx48YhICAACoUCrVq1wtKlS2FnZ1dM0RMREZVMzL9ERETqIxEit6k9CQBevXoFKysrPH36NM+ZUkNDQ+Hu7s5JCoqIbag6tqHq2IbqwXZUXUHb0NzcvNQ+/oW5t3iwHVXHNlQd21B1bEPVFaYNi5J/dfYOty5ISEgAAOWYNSIiIl3w6tWrXIvR0oC5l4iIdFVR8i/vcOdDoVDg+fPneV7JyHpWaF5X4en92IaqYxuqjm2oHmxH1RW0DUvzHW7m3uLBdlQd21B1bEPVsQ1VV5g25B1uNZNKpahQocJ7t7OwsOAbXEVsQ9WxDVXHNlQPtqPqynIbMvcWL7aj6tiGqmMbqo5tqDpNtaFOPhaMiIiIiIiIqKRjwU1ERERERESkASy4VWBoaIhZs2bB0NBQ26GUWGxD1bENVcc2VA+2o+rYhu/HNlIPtqPq2IaqYxuqjm2oOk23ISdNIyIiIiIiItIA3uEmIiIiIiIi0gAW3EREREREREQawIKbiIiIiIiISANYcOciLS0NkydPhrOzM4yNjeHt7Y1jx44VaN+wsDD07NkTVlZWsLCwQKdOnfDgwQMNR6x7itqG+/btQ69eveDm5gYTExNUr14dEyZMQFxcnOaD1jGqvA/f1qZNG0gkEnz11VcaiFK3qdqGu3btQpMmTWBqagorKys0bdoUJ06c0GDEukmVdjx+/Dg++ugj2NrawsrKCo0aNcKWLVs0HLFuSUxMxKxZs9CuXTtYW1tDIpFg48aNBd4/Li4OI0aMgJ2dHUxNTfHRRx/h8uXLmgtYS5h71YP5V3XMv6pj/lUdc6/qdCb/Csqhd+/eQk9PT/j7+4u1a9eKJk2aCD09PXHmzJl890tISBBVq1YV9vb2YsGCBWLJkiXCxcVFVKhQQbx8+bKYotcNRW1DGxsb4eHhIWbMmCHWr18vxowZIwwMDESNGjVEcnJyMUWvG4rahm/bu3evMDU1FQDEl19+qcFodZMqbThr1iwhkUhEjx49xJo1a8SPP/4oRo4cKTZv3lwMkeuWorbjb7/9JiQSiWjatKn48ccfxcqVK0WLFi0EALFkyZJiil77Hj58KACIihUrilatWgkAIjAwsED7yuVy0bRpU2Fqaiq+/fZbsXLlSlGrVi1hbm4u7t69q9nAixlzr3ow/6qO+Vd1zL+qY+5Vna7kXxbc77hw4YIAIBYtWqRclpKSItzd3UWTJk3y3XfBggUCgLh48aJyWUhIiJDJZGLq1Kkai1nXqNKGJ0+ezLFs06ZNAoBYv369ukPVWaq04dvbu7q6iu+++65MJnxV2vD8+fNCIpGUucSUG1XasU2bNsLZ2VmkpqYql2VkZAh3d3dRt25djcWsa1JTU0V4eLgQQohLly4VKuHv2rVLABC//vqrctmLFy+ElZWV6NOnjybC1QrmXvVg/lUd86/qmH9Vx9yrHrqSf1lwv2PixIlCJpOJV69eZVs+b948AUA8efIkz329vLyEl5dXjuW+vr7C3d1d7bHqKlXaMDfx8fECgBg/frw6w9Rp6mjD2bNni4oVK4rk5OQymfBVacNevXoJJycnIZfLhUKhEAkJCZoOV2ep0o7e3t6idu3auS739vZWe6wlQWETfo8ePYSDg4OQy+XZlo8YMUKYmJhk+0JVkjH3qgfzr+qYf1XH/Ks65l7102b+5Rjud1y5cgXVqlWDhYVFtuWNGjUCAFy9ejXX/RQKBa5fv46GDRvmWNeoUSOEhoYiISFB7fHqoqK2YV4iIiIAALa2tmqJryRQtQ2fPHmC77//HgsWLICxsbGmwtRpqrThn3/+CS8vL6xYsQJ2dnYwNzeHk5MTVq5cqcmQdZIq7diqVSv8999/mDFjBu7fv4/Q0FDMmTMHwcHBmDRpkibDLjWuXLmCBg0aQCrNnq4bNWqE5ORk3L17V0uRqRdzr3ow/6qO+Vd1zL+qY+7VPnXmXz11B1fShYeHw8nJKcfyrGXPnz/Pdb+YmBikpaW9d9/q1aurMVrdVNQ2zMuCBQsgk8nQvXt3tcRXEqjahhMmTED9+vXRu3dvjcRXEhS1DWNjY/Hy5Uv8/fffOHHiBGbNmoWKFSsiMDAQo0ePhr6+PkaOHKnR2HWJKu/FGTNm4OHDh5g7dy4CAgIAACYmJti7dy86deqkmYBLmfDwcLRo0SLH8rfb38PDo7jDUjvmXvVg/lUd86/qmH9Vx9yrferMvyy435GSkgJDQ8Mcy42MjJTr89oPQJH2LW2K2oa52b59OzZs2IBJkyahatWqaotR16nShidPnsTevXtx4cIFjcVXEhS1DRMTEwEA0dHR2LlzJ3r16gUA6N69Ozw8PBAQEFBmEj6g2nvR0NAQ1apVQ/fu3dG1a1fI5XKsW7cO/fr1w7Fjx9C4cWONxV1aqPPzVJcx96oH86/qmH9Vx/yrOuZe7VPn5ykL7ncYGxsjLS0tx/LU1FTl+rz2A1CkfUuborbhu86cOYOhQ4eibdu2mDt3rlpj1HVFbcPMzEyMGTMG/fv3h5eXl0Zj1HWq/i3r6+tnu6sjlUrRq1cvzJo1C0+ePEHFihU1ELXuUeXv+auvvsI///yDy5cvK7tk9ezZE7Vr18bYsWPL/JfSglDX56muY+5VD+Zf1TH/qo75V3XMvdqnzvzLMdzvcHJyQnh4eI7lWcucnZ1z3c/a2hqGhoZF2re0KWobvu3atWvo2LEj6tSpgz179kBPr2xdGypqG27evBl37tzByJEj8ejRI+UPACQkJODRo0dITk7WWNy6RJW/ZSMjI9jY2EAmk2VbZ29vD+B1t7eyoqjtmJ6ejg0bNuDTTz/NNv5JX18fn3zyCYKDg5Genq6ZoEsRdXyelgTMverB/Ks65l/VMf+qjrlX+9SZf1lwv8PT0xN3795FfHx8tuVZV4M8PT1z3U8qlcLDwwPBwcE51l24cAFubm4wNzdXe7y6qKhtmCU0NBTt2rWDvb09jh49CjMzM02FqrOK2oZPnjxBRkYGPvzwQ1SuXFn5A7z+MlC5cmUEBQVpNHZdocrfsqenJ6KionIkpawxU3Z2duoPWEcVtR2jo6ORmZkJuVyeY11GRgYUCkWu6yg7T09PXL58GQqFItvyCxcuwMTEBNWqVdNSZOrF3KsezL+qY/5VHfOv6ph7tU+t+bcw06mXBf/880+O596lpqaKKlWqZJtK//HjxyIkJCTbvt9//70AIC5duqRcdvv2bSGTycTkyZM1H7yOUKUNw8PDhZubm3B2dhYPHz4srpB1TlHbMCQkROzfvz/HDwDRvn17sX//fvH8+fNiPRdtUeV9uHTpUgFArFu3TrksJSVFuLm5iVq1amk+eB1S1HbMzMwUVlZWolq1aiItLU25PCEhQVSoUEHUqFGjeE5Ax+T3WJLnz5+LkJAQkZ6erly2c+fOHM8BjYqKElZWVqJXr17FEXKxYO5VD+Zf1TH/qo75V3XMveqnzfzLgjsXPXr0EHp6emLixIli7dq1omnTpkJPT0+cPn1auU3Lli3Fu9cr4uPjhbu7u7C3txcLFy4US5cuFS4uLsLZ2Vm8ePGiuE9Dq4rahvXq1RMAxKRJk8SWLVuy/QQFBRX3aWhVUdswNyiDzwEVouhtmJycLGrXri309fWFv7+/WLFihfDy8hIymUwcPXq0uE9D64rajgEBAQKAqF+/vli6dKlYvHixqFmzpgAgtm7dWtynoVU//vijmDNnjhg1apQAILp27SrmzJkj5syZI+Li4oQQQgwcOFAAyFbsZGZmisaNGwszMzMxe/ZssWrVKlG7dm1hbm4ubt++raWz0QzmXvVg/lUd86/qmH9Vx9yrHrqQf1lw5yIlJUX4+/sLR0dHYWhoKLy8vMT//ve/bNvk9UH79OlT0b17d2FhYSHMzMyEn5+fuHfvXnGFrjOK2oYA8vxp2bJlMZ6B9qnyPnxXWU34qrRhZGSkGDhwoLC2thaGhobC29s7x75lhSrtuG3bNtGoUSNhZWUljI2Nhbe3t9izZ09xha4zKlWqlOdnW1aCzy3hCyFETEyMGDp0qLCxsREmJiaiZcuW2e7mlhbMverB/Ks65l/VMf+qjrlXPXQh/0qEEKLgHdCJiIiIiIiIqCA4aRoRERERERGRBrDgJiIiIiIiItIAFtxEREREREREGsCCm4iIiIiIiEgDWHATERERERERaQALbiIiIiIiIiINYMFNREREREREpAEsuImIiIiIiIg0gAU3ERERERERkQaw4Cad5+rqColEku3H0NAQFStWRK9evXDmzBlth6j07bffQiKR4Ntvv822fOPGjZBIJBg0aJBW4lKHvM6tpMl6Pz169Ejjr/Xo0SNIJBK4uroWet+84hw0aBAkEgk2btyYbXlpeI8RkW5h/tUNzL+Fx/xLuoQFN5UYH374IQYOHIiBAwfik08+gUKhwO7du9GyZUssWbJE2+EVm+JMWFTyqfKlg4gIYP7NwvxLhcH8S1n0tB0AUUENGzYs29XD1NRUjBw5Eps3b8akSZPg5+eHatWqaS/AfHTp0gWNGzeGpaWltkOhEuLPP/9ERkYGypcvX6Dt+R4jIk1h/qWyhPmX1I13uKnEMjIywqpVq2Bqagq5XI59+/ZpO6Q8WVpaokaNGnByctJ2KFRCuLu7o0aNGtDX1y/Q9nyPEVFxYf6l0oz5l9SNBTeVaGZmZqhevToAZOvilTXWDAACAwPRpEkTWFpa5ugK9vz5c4wfPx41a9aEiYkJzM3N4eXlhZUrVyIzMzPX10xJScG3336LqlWrwtDQEE5OThg4cCCePHmSZ5zvG98TFhaGiRMnwsPDA+bm5jA1NUW1atUwaNAgnDt3LtsxHj9+DACoXLlytnF1p06dynbM4jq3/Lx93tHR0fjyyy9RsWJFGBoaolKlShg3bhxiY2Nz7Hfq1ClIJBK0atUKycnJmDlzpvI83u2atXPnTvj4+MDa2lp53CFDhuDu3bvvjW///v1o1qwZLCwsYG5ujlatWuHo0aO5bvv48WMsWLAArVu3Vp6DlZUVmjVrhrVr10KhUOT7WpmZmVi4cCFq164NY2Nj2NraomfPnrh9+3au2xe262Ju77FBgwahcuXKyvjfHYsJAAMHDoREIsH8+fPzPPbu3bshkUjQqFGjAsVCRKUf8y/zL/Pva8y/9D7sUk4lXnx8PADA0NAwx7rRo0dj9erVaNq0KT799FM8ePBA+UH3119/oXPnzoiNjYWrqyvatGmDtLQ0XLx4EaNHj8ahQ4dw+PDhbFc4k5OT4ePjg3/++Qempqbw9fWFsbEx/vjjDxw5cgSffvppoeP/888/0b17d8TFxcHe3h4+Pj4wMDDAo0ePsH37dgBA06ZNUaVKFQwcOBB79uxBUlISunXrBjMzM+VxHB0dlf+vK+eWJTY2Ft7e3oiOjkarVq2UX1CWLVuG33//HWfOnIGdnV2O/VJTU9GqVSvcunULLVq0QL169RAdHQ0AEEJg0KBB2Lx5M/T09NCiRQvY29vj8uXLCAwMxK5du7B37160a9cu15hWrFiBpUuXomHDhvDz80NoaChOnz6N06dPY8WKFRg9enS27bds2YIZM2agcuXKqFatGj788EOEh4fj/Pnz+PvvvxEUFIQ9e/Yo31/v6tWrFw4dOoSWLVuibt26uHjxIn799Vf8/vvvCAoKQpMmTYrcvnlp1qwZEhMTsXfvXpiamqJ79+45thk7diw2b96MNWvWYNKkSZDJZDm2WbVqFQDgq6++UnuMRFRyMf++xvzL/Psu5l/KRhDpuEqVKgkAIjAwMMe6a9euCalUKgCIX375RbkcgAAgLCwsxPnz53PsFx4eLmxsbIREIhGrV68Wcrlcue7ly5eidevWAoCYPXt2tv38/f0FAFGjRg0RFhamXJ6UlCQ6deqkfN1Zs2Zl2y8wMFAAEAMHDsy2/MmTJ8LS0lIAEFOmTBFpaWnZ1kdGRoozZ87k2h4PHz7MrbmK/dzyk3XeAETjxo1FdHS0cl1sbKxo2rSpACB69+6dbb+TJ08q96tbt64IDw/PceyffvpJABC2trbiypUryuUKhULMmjVLABBWVlbixYsX2fbLaj+JRCK2bt2abd3OnTuFRCIRenp64saNG9nWXbx4MccyIYQICwsT9erVEwDE7t27s617+PCh8jxsbW3FtWvXlOsyMzPF6NGjBQBRqVIlkZqammuc7/47Dxw4MNe/h7zeY1kxVKpUKUfsWT788EMBQOzbty/Huhs3bggAws7OLkeMRFS6Mf8y/zL//j/mXyoqFtyk83JL+HFxceLIkSPC3d1dABDOzs4iMTFRuT7rQ/a7777L9ZiTJ08WAMRXX32V6/pnz54JfX19YWdnJxQKhRBCiOTkZGFubi4AiN9//z3HPuHh4cLIyKhQCf/rr78WAESHDh0K0BKvvS/hF/e55efthP92Us5y/fp1IZFIhFQqFU+fPlUufzvh//XXX7keO+vffsWKFTnWKRQKUbduXQFAzJ07N9u6rPbr3Llzrsft1q2bACCGDx9e4PP8448/BADRo0ePbMvfTvjLli3LsV9qaqooX768ACC2bduWa5zFkfB3794tAAgfH58c60aOHCkAiKlTp+a5PxGVTsy/2TH/vsb8G5htOfMvvQ/HcFOJMXjwYOXYFysrK3z66acIDQ2Fu7s7jh49ClNT0xz75NaFBwCOHDkC4HU3o9yUL18eVatWRVRUFO7duwcAuHz5MhISEmBra5trNylHR0f4+voW6pz+97//AQBGjBhRqP3yoyvn9rZ69erB09Mzx3IPDw/Ur18fCoUCf/31V4719vb2aN68eY7lz549Q2hoKIDXY6DeJZFIMHjwYADAyZMnc40pt/3eXv7umDwASEtLw6FDhzBz5kx8/vnnGDx4MAYNGoS1a9cCAO7cuZPrMfN6PUNDQ+W/U26vV1y6dOkCFxcX/Pnnn9nGtL169Qpbt26FTCbDqFGjtBYfEWkX82/B6Mq5vY35l/mXtI9juKnE+PDDD1GlShUAgIGBAezt7dG4cWO0a9cOenq5v5XzevbhgwcPACDXZPKuqKgoVKtWDc+ePcv3mACUE2QUVNYELDVq1CjUfvnRlXMr6L6VK1fG5cuXlTG8La94wsLCAAA2NjawsLDIdRt3d/ds2xY0pqzl78bzzz//oFevXvlOYJM1nvFdVlZWsLKyKtTrFSc9PT188cUXmDp1KlauXImVK1cCADZt2oSkpCTlFwIiKpuYfwtGV86toPsy/zL/UvFgwU0lxrvPAS0IY2PjXJdnzWjZvXv3XK/Mv83GxqZQr6ltJfXchBA5luX171cc3o4nOTkZnTt3RmRkJAYPHoxRo0ahSpUqsLCwgEwmw927d1G9evVcz6Eor6cNw4cPx3fffYfNmzdj/vz5MDMzw+rVqwFwshaiso75t2BK6rkx/zL/kmax4KYyycXFBffu3cPkyZPRsGHDAu1Tvnx5AMj3MREFfYRElooVK+LOnTu4ffu28u6BqnTl3N728OHD9x63QoUKBT5eVrzR0dGIj4/P9Sp71p2GrG1zi6levXoFiuevv/5CZGQkGjRogF9++SXHPlldA/MSFxeHuLi4XK+yF+X8NcHGxgZ9+/bFzz//jM2bN6NatWq4c+cOatWqhdatW2s1NiIqPXQlRzH/Mv8y/1Jx4RhuKpM++eQTAK+fb1hQH3zwAczMzPDy5UsEBQXlWB8ZGZnr8vxkjddav359gfcxMDAAgDyf5akr5/a269ev4/r16zmW//fff7h8+TKkUilatGhR4ONVqFBB2WVt48aNOdYLIZTLP/roo1yPsWXLllyXb968GQDQqlUr5bKYmBgAr7+g5Wbr1q3vjTm310tPT8euXbtyvJ46ve/98rYxY8YAeP0YkqxubV9++aVG4iKisklXchTzL/Mv8y8VFxbcVCZNnDgRVlZWWLJkCX744Qekp6fn2Obhw4fZPsiNjY2Vk6uMGzcO4eHhynUpKSkYNWoUUlJSChXH+PHjYW5ujoMHD2L69OnIyMjItv7Fixc4e/ZstmVZV2L/++8/nT63twkhMGrUKMTGxiqXvXr1CqNGjYIQAt26dSv0GCV/f38AwJw5c3Dt2rVsrxUQEICrV6/CysoKw4cPz3X//fv3Y+fOndmW7dmzB3v37oWenl6254DWrFkTwOtntt66dSvbPuvWrVMm7fzMmTMHN2/eVP6uUCgwefJkPHv2DC4uLujWrdt7j1EUdnZ2MDAwQEREhPKLS148PDzQunVrhISE4ODBg7CwsMCAAQM0EhcRlU26kqOYf5l/mX+p2BT/xOhEhZPfc0DzgjePgsjP6dOnha2trQAg7O3tRevWrUXfvn2Fn5+f8pEX3t7e2fZJTEwUjRo1EgCEmZmZ6NChg+jRo4dwdHQUNjY2YsCAAYV6LIkQrx9pkfVIEAcHB9G5c2fRo0cP0ahRI6Gvr59jn5UrVypfv2vXrmLo0KFi6NCh4vbt21o5t/xknXfHjh2Fm5ubsLKyEl26dBFdu3YV1tbWAoCoWrWqiIyMzLZf1mNJWrZsmeexFQqF6N+/vwAg9PT0hI+Pj+jTp4+oXr26ACCMjY3F0aNHc+yX9X7KeiSMl5eX+Oyzz4S3t7fyfbNkyZIc+2U9C9XAwED4+vqK3r17ixo1agiJRCK++eabXB/9kfVIkIoVK4ouXboIfX190aZNG9G7d2/lv4OpqWmOZ72+HaeqjyURQoju3bsLAMLFxUX06dNH+Z7JzYEDB5TtMHr06Fy3IaKygfk3+z7Mv68x/wbm2tbMv5QXFtyk8zSV8IUQIjIyUsyYMUM0aNBAmJubCwMDA1GhQgXRtGlTMWvWLHH9+vUc+yQlJYkZM2YId3d3YWBgIBwcHETfvn3Fw4cPxaxZswqd8IUQ4vHjx2Ls2LGievXqwsjISJiZmYlq1aqJIUOGiPPnz2fbVi6Xi/nz54vatWsrn80JQJw8eVIr55aft8/7xYsXYuTIkaJChQrCwMBAuLi4iDFjxojo6Ogc+xUk4WfZvn27aNWqlbCyshL6+vrCxcVFDBo0KNsXoLe9nUh3794tmjRpIszMzISpqalo3ry5OHToUK77paeni0WLFgkPDw9hYmIirK2tha+vrwgKCsrzWZtvL8/IyBBz584VNWrUEIaGhsLa2lp069ZN/Pfff++N821FSfjR0dFi5MiRomLFikJfXz/fv4+EhAQhk8mERCLJsw2JqGxg/mX+zQ/z72vMv/Q+EiG0PDUfEZVaGzduxODBgzFw4MBcx3qR7vn5558xfPhw+Pr64o8//tB2OEREVATMvyUP82/pxTHcREQEAEhKSsL8+fMBABMmTNByNERERGUD82/pxseCERGVcYsWLcLNmzdx9uxZPHjwAO3atYOvr6+2wyIiIirVmH/LBhbcRERl3JEjR3D69GnY2tpi0KBBWLJkibZDIiIiKvWYf8sGjuEmIiIiIiIi0gCO4SYiIiIiIiLSABbcRERERERERBrAgpuIiIiIiIhIA1hwExEREREREWkAC24iIiIiIiIiDWDBTURERERERKQBLLiJiIiIiIiINIAFNxEREREREZEGsOAmIiIiIiIi0oD/A8ROIgnpSkP1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cal_curves = bf.diagnostics.plot_calibration_curves(sim_indices, model_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = bf.diagnostics.plot_confusion_matrix(sim_indices, model_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our neural network quickly learned to discriminate between the two hierarchical models and shows excellent performance when validated on simulated data. The calibration curves look a bit shaky, but the marginal bin histograms tell us that this is due to the majority of the predicted probabilities being close to 0 or 1, leaving the middle ('uncertain') bins quite abandonded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in [Part 1](./Model_Comparison_MPT.ipynb), we apply our trained model to a synthetic data set from the **2HT** model. We again redefine our simulator with fixed random seeds for reproducible results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 50, 100, 2)\n"
     ]
    }
   ],
   "source": [
    "prior_fixed = bf.simulation.Prior(\n",
    "    prior_fun=partial(hierarchical_prior_fun, rng=np.random.default_rng(2023)), param_names=PARAM_NAMES\n",
    ")\n",
    "fake_data_generator = bf.simulation.GenerativeModel(\n",
    "    prior=prior_fixed,\n",
    "    simulator=partial(\n",
    "        hierarchical_mpt_simulator, model=\"2HT\", num_groups=N_GROUPS, num_obs=N_OBS, rng=np.random.default_rng(2023)\n",
    "    ),\n",
    "    skip_test=True,\n",
    "    simulator_is_batched=False,\n",
    ")\n",
    "\n",
    "fake_data = fake_data_generator(batch_size=1)[\"sim_data\"]\n",
    "print(fake_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect our simulated data set by looking at hit and false alarm rates for each of our 50 participants. This is best done visually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rates = get_rates(fake_data)\n",
    "f, ax = plt.subplots(1, 2, figsize=(8, 3))\n",
    "sns.histplot(rates[0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax[0]).set(title=\"Hit Rates\")\n",
    "sns.histplot(rates[1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=ax[1]).set(\n",
    "    title=\"False Alarm Rates\"\n",
    ")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in [part 1](./Model_Comparison_MPT.ipynb), our data set contains many participants with low false alarm rates, which are unlikely under the 1HT model. Let's see the evidence contained in our simulated data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.00586275, 0.9941373 ], dtype=float32)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings = summary_net(fake_data)\n",
    "preds = inference_net.posterior_probs(embeddings)[0]\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.005897322>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_factor12 = preds[0] / preds[1]\n",
    "bayes_factor12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Bayesian model comparison reveals clear evidence for the 2HT model, much more decisive than in [Part 1](./Model_Comparison_MPT.ipynb). While the unambiguousness of the results depends, of course, on the model specification and the randomly simulated data, our model comparison now contains $50$ times more data by considering all participants simultaneously!"
   ]
  }
 ],
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